Neural networks have been successfully applied in various resource-constrained edge devices, where usually central processing units (CPUs) instead of graphics processing units exist due to limited power availability. State-of-the-art research still focuses on efficiently executing enormous numbers of multiply-accumulate (MAC) operations. However, CPUs themselves are not good at executing such mathematical operations on a large scale, since they are more suited to execute control flow logic, i.e., computer algorithms. To enhance the computation efficiency of neural networks on CPUs, in this paper, we propose to convert them into logic flows for execution. Specifically, neural networks are first converted into equivalent decision trees, from which decision paths with constant leaves are then selected and compressed into logic flows. Such logic flows consist of if and else structures and a reduced number of MAC operations. Experimental results demonstrate that the latency can be reduced by up to 14.9 % on a simulated RISC-V CPU without any accuracy degradation. - The code is open source at https://github.com/TUDa-HWAI/NN2Logic

In High-Level Synthesis (HLS), refactoring a standard C/C++ code into its HLS-compatible version (HLS-C) still requires significant human effort. While various program scripts have been introduced to automate this process, the resulting code still contains many HLS-incompatible issues that need to be manually refactored and optimized by developers. Since Large Language Models (LLMs) have the ability to automate code generation, they can also be used for automated code refactoring and optimization in HLS. However, due to the limited training of LLMs, considering hardware and software simultaneously, hallucinations may occur when using LLMs for HLS, leading to synthesis failures. To address these challenges, we introduce HLSRewriter , an LLM-aided code refactoring and optimization framework that takes regular C/C++ code as input and automatically generates its corresponding optimized HLS-C code for hardware synthesis with minimal human intervention. To mitigate LLM hallucinations, a step-wise reasoning process is employed to analyze and detect HLS-incompatible errors. Afterwards, a repair library containing reference templates is efficiently created by scanning the HLS tool manual, followed by cooperation with a Retrieval-Augmented Generation (RAG) paradigm to guide the LLMs toward correct refactoring. In addition, a pipeline-aware decomposition strategy is introduced to progressively break down complex loop structures into smaller tasks with a balanced trade-off between latency and area, thereby enabling efficient pipelining and parallel execution. To further improve hardware efficiency, a bit width adjuster module is incorporated into this framework to optimize the precision of floating-point variables. Moreover, LLM-aided HLS optimization strategies are introduced to add/tune hardware directives in HLS-C code, thereby enhancing the performance of the final synthesized hardware. Experimental results demonstrate that the proposed LLM-aided framework can achieve higher refactoring pass rates and superior hardware performance in 24 real-world tasks compared with traditional approaches and the direct application of LLMs for code refactoring and optimization. The codes are open-sourced at this link: https://github.com/code-source1/catapult .

General-purpose optical accelerators (GOAs) have emerged as a promising platform to accelerate deep neural networks (DNNs) due to their low latency and energy consumption. Such an accelerator is usually composed of a given number of interleaving Mach-Zehnder-Interferometers (MZIs). This interleaving architecture, however, has a low efficiency when accelerating neural networks of various sizes due to the mismatch between weight matrices and the GOA architecture. In this work, a hybrid GOA architecture is proposed to enhance the mapping efficiency of neural networks onto the GOA. In this architecture, independent MZI modules are connected with microring resonators (MRRs), so that they can be combined to process large neural networks efficiently. Each of these modules implements a unitary matrix with inputs adjusted by tunable coefficients. The parameters of the proposed architecture are searched using genetic algorithm. To enhance the accuracy of neural networks, selected weight matrices are expanded to multiple unitary matrices applying singular value decomposition (SVD). The kernels in neural networks are also adjusted to use up the on-chip computational resources. Experimental results show that with a given number of MZIs, the mapping efficiency of neural networks on the proposed architecture can be enhanced by 21.87%, 21.20%, 24.69%, and 25.52% for VGG16 and Resnet18 on datasets Cifar10 and Cifar100, respectively. The energy consumption and computation latency can also be reduced by over 67% and 21%, respectively.

Functional simulation is an essential step in digital hardware design. Recently, there has been a growing interest in leveraging Large Language Models (LLMs) for hardware testbench generation tasks. However, the inherent instability associated with LLMs often leads to functional errors in the generated testbenches. Previous methods do not incorporate automatic functional correction mechanisms without human intervention and still suffer from low success rates, especially for sequential tasks. To address this issue, we propose CorrectBench, an automatic testbench generation framework with functional self-validation and self-correction. Utilizing only the RTL specification in natural language, the proposed approach can validate the correctness of the generated testbenches with a success rate of 88.85 %. Furthermore, the proposed LLM-based corrector employs bug information obtained during the self-validation process to perform functional self-correction on the generated testbenches. The comparative analysis demonstrates that our method achieves a pass ratio of 70.13 % across all evaluated tasks, compared with the previous LLM-based testbench generation framework's 52.18% and a direct LLM-based generation method's 33.33%. Specifically in sequential circuits, our work's performance is 62.18 % higher than previous work in sequential tasks and almost 5 times the pass ratio of the direct method. The codes and experimental results are open-sourced at the link: https://github.com/AutoBench/CorrectBench.

Large Language Models (LLMs) have achieved remarkable breakthroughs. However, the huge number of parameters in LLMs require significant amount of memory storage in inference, which prevents their practical deployment in many applications. To reduce memory storage of LLMs, singular value decomposition (SVD) provides a promising solution to approximate weight matrices for compressing LLMs. In this paper, we take a step further to explore parameter sharing across different layers with SVD to achieve more effective compression for LLMs. Specifically, weight matrices in different layers are decomposed and represented with a linear combination of a set of shared basis vectors and unique coefficients. The types of weight matrices and the layer selection for basis sharing are examined when compressing LLMs to maintain the performance. Comprehensive experiments demonstrate that Basis-Sharing outperforms state-of-the-art SVD-based compression approaches, especially at large compression ratios.

Content addressable memory (CAM) is widely used in advanced machine learning models and data-intensive applications for associative search tasks, thanks to the highly parallel pattern matching capability. Most state-of-the-art CAM designs primarily aim to reduce the CAM cell area by utilizing nonvolatile memories (NVMs). However, there has been limited research on optimizing the design and energy efficiency of NVM-based CAMs for practical deployment in edge devices and AI hardware. This article introduces a general compact and energy efficient CAM design scheme that minimizes design overhead by using only one NVM device per cell. Our proposed CAM design realizes both binary CAM (BCAM) and multibit CAM (MCAM) by leveraging the binary and multilevel storage property of NVM devices without additional cell overheads. Additionally, we propose an adaptive matchline (ML) precharge and discharge scheme to further optimize search energy by significantly reducing the ML voltage swing. Ferroelectric field-effect transistors (FeFETs) serve as representative NVMs in our proposed design, and we present a 2T-1FeFET CAM array incorporating a sense amplifier that implements the proposed ML scheme. Evaluation results show that our proposed 2T-1FeFET BCAM design achieves energy efficiency improvements of$6.64\times $/$4.74\times $/$9.14\times $/$3.02\times $compared to CMOS/ReRAM/STT-MRAM/2FeFET BCAM arrays, while 2T-1FeFET MCAM design achieves$8.25\times $/$5.68\times $/$56.35\times $better-energy efficiency compared to ReRAM/3T-1FeFET/1FeFET-1R MACM arrays. Benchmarking results demonstrate that our BCAM/MCAM approach provides$3.2\times $/$3.7\times $and$2.0\times $/$2.2\times $energy-delay product improvement over the 2T-2R and 2FeFET CAM in accelerating query processing applications.

Resistive random access memory (RRAM) has emerged as a promising technology for deep neural network (DNN) accelerators, but programming every weight in a DNN onto RRAM cells for inference can be both time-consuming and energy-intensive, especially when switching between different DNN models. This article introduces a hardware-aware multimodel merging (HA3M) framework designed to minimize the need for reprogramming by maximizing weight reuse, while taking into account the hardware constraints of the accelerator. The framework includes three key approaches: 1) crossbar (XB)-aware model mapping (XAMM); 2) block-based layer matching (BLM); and 3) multimodel retraining (MMR). XAMM reduces the XB usage of the preprogrammed model on RRAM XBs while preserving the model’s structure. BLM reuses preprogrammed weights in a block-based manner, ensuring the inference process remains unchanged. MMR then equalizes the block-based matched weights across multiple models. Experimental results show that the proposed framework significantly reduces programming cycles in multi-DNN switching scenarios while maintaining or even enhancing accuracy, and eliminating the need for reprogramming.

Neural networks (NNs) have been successfully deployed in various fields. In NNs, a large number of multiply-accumulate (MAC) operations need to be performed. Most existing digital hardware platforms rely on parallel MAC units to accelerate these MAC operations. However, under a given area constraint, the number of MAC units in such platforms is limited, so MAC units have to be reused to perform MAC operations in a neural network. Accordingly, the throughput in generating classification results is not high, which prevents the application of traditional hardware platforms in extreme-throughput scenarios. Besides, the power consumption of such platforms is also high, mainly due to data movement. To overcome this challenge, in this paper, we propose to flatten and implement all the operations at neurons, e.g., MAC and ReLU, in a neural network with their corresponding logic circuits. To improve the throughput and reduce the power consumption of such logic designs, the weight values are embedded into the MAC units to simplify the logic, which can reduce the delay of the MAC units and the power consumption incurred by weight movement. The retiming technique is further used to improve the throughput of the logic circuits for neural networks. In addition, we propose a hardware-aware training method to reduce the area of logic designs of neural networks. Experimental results demonstrate that the proposed logic designs can achieve high throughput and low power consumption for several high-throughput applications.

Deep neural networks (DNNs) have been widely deployed across diverse domains such as computer vision and natural language processing. However, the impressive accomplishments of DNNs have been realized alongside extensive computational demands, thereby impeding their applicability on resource-constrained devices. To address this challenge, many researchers have been focusing on basic neuron structures, the fundamental building blocks of neural networks, to alleviate the computational and storage cost. In this work, an efficient quadratic neuron architecture distinguished by its enhanced utilization of second-order computational information is introduced. By virtue of their better expressivity, DNNs employing the proposed quadratic neurons can attain similar accuracy with fewer neurons and computational cost. Experimental results have demonstrated that the proposed quadratic neuron structure exhibits superior computational and storage efficiency across various tasks when compared with both linear and non-linear neurons in prior work.

The exponential growth of data across various domains of human society necessitates the rapid and efficient data processing. In many contemporary data-intensive applications, similarity computation (SC) is one of the most fundamental and indispensable operations. In recent years, In-memory computing (IMC) architectures have been designed to accelerate SC by reducing data movement costs, however, they encounter challenges with signal domain conversion, variation sensitivity, and limited precision. This paper proposes a ferroelectric FET (FeFET) based time-domain (TD) associative memory (AM) for energy efficient SC. Such TD design can convert its output (i.e., time interval) to digits with relatively simple sensing circuitry thus saves large amount of area and energy compared with conventional IMC designs that process analog voltage/current signals. The variable-capacitance (VC) delay chain structure in our design supports quantitative SC and enhances robustness against variations. Furthermore, by exploiting multi-domain ferroelctric FET (FeFET), our design is capable of performing SC on vectors with multi-bit element, enabling support for higher-precision algorithms. Simulation results show that the proposed TD-AM achieves 13.8x/1.47x energy saving of our design compared to CMOS/NVM based TD-IMC designs. Additionally, our design exhibits good robustness in monte carlo simulation with variation extracted from experimental measurements. Investigation on precision of hyperdimensional computing (HDC) show that higher element precision reduces the size of HDC model when considering to achieve same accuracy, indicating an improved efficiency. Benchmarkings against GPU demonstrate in general 2/3 orders of magnitude speedup/energy efficiency improvement of our design. Our proposed multi-bit TD-AM promises energy-efficient quantitative SC for diverse intensive data processing application, especially in energy-constrained scenarios.

Scan chain is a commonly used technique in testing integrated circuits as it provides observability and controllability of the internal states of circuits. However, its presence can make circuits vulnerable to attacks and potentially result in confidential internal data leakage. In this paper, we propose a novel technique for obfuscating scan chains using camouflaged flip-flops, which are designed with the same layout as the original flip-flops but have the actual functionality of a buffer. Furthermore, we employ camouflaged logic gates interconnected in special configurations to increase the difficulty of SAT attack. Experimental results demonstrate that circuits with only a small number of flip-flops can already be protected by the proposed technique while incurring only a minimal area overhead.

Having the potential for high speed, high throughput, and low energy cost, optical neural networks (ONN s) have emerged as a promising candidate for accelerating deep learning tasks. In conventional ONNs, light amplitudes are modulated at the input and detected at the output. However, the light phases are still ignored in conventional structures, although they can also carry information for computing. To address this issue, in this paper, we propose a framework called OplixNet to compress the areas of ONNs by modulating input image data into the amplitudes and phase parts of light signals. The input and output parts of the ONN s are redesigned to make full use of both amplitude and phase information. Moreover, mutual learning across different ONN structures is introduced to maintain the accuracy. Experimental results demonstrate that the proposed framework significantly reduces the areas of ONNs with the accuracy within an acceptable range. For instance, 75.03 % area is reduced with a 0.33% accuracy decrease on fully connected neural network (FCNN) and 74.88% area is reduced with a 2.38% accuracy decrease on ResNet-32.

Deep neural networks (DNNs) have made breakthroughs in various fields including image recognition and language processing. DNNs execute hundreds of millions of multiply-and-accumulate (MAC) operations. To efficiently accelerate such computations, analog in-memory-computing platforms have emerged leveraging emerging devices such as resistive RAM (RRAM). However, such accelerators face the hurdle of being required to have sufficient on-chip crossbars to hold all the weights of a DNN. Otherwise, RRAM cells in the crossbars need to be reprogramed to process further layers, which causes huge time/energy overhead due to the extremely slow writing and verification of the RRAM cells. As a result, it is still not possible to deploy such accelerators to process large-scale DNNs in industry. To address this problem, we propose the BasisN framework to accelerate DNNs on any number of available crossbars without reprogramming. BasisN introduces a novel representation of the kernels in DNN layers as combinations of global basis vectors shared between all layers with quantized coefficients. These basis vectors are written to crossbars only once and used for the computations of all layers with marginal hardware modification. BasisN also provides a novel training approach to enhance computation parallelization with the global basis vectors and optimize the coefficients to construct the kernels. Experimental results demonstrate that cycles per inference and energy-delay product were reduced to below 1% compared with applying reprogramming on crossbars in processing large-scale DNNs such as DenseNet and ResNet on ImageNet and CIFAR100 datasets, while the training and hardware costs are negligible.

The last decade has witnessed the breakthrough of deep neural networks (DNNs) in many fields. With the increasing depth of DNNs, hundreds of millions of multiply-and-accumulate (MAC) operations need to be executed. To accelerate such operations efficiently, analog in-memory computing platforms based on emerging devices, e.g., resistive RAM (RRAM), have been introduced. These acceleration platforms rely on analog properties of the devices and thus suffer from process variations. Consequently, weights in neural networks configured into these platforms can deviate from the nominal trained values, which may lead to feature errors and a significant degradation of the inference accuracy. Besides, additional HW aspects represent key controlling factors for such computing platforms, namely, the limited RRAM cell programmable conductance levels, which limits the number of bits stored in one RRAM cell, the ADC noise converting analog values to digital domain and the ADC power scaling with the number of bits of its output. To address these points, in this article, we propose a framework to enhance the robustness of neural networks under variations. First, an enhanced Lipschitz constant regularization is adopted during neural network training to suppress the amplification of errors propagated through network layers. Additionally, the quantization setting of a NN model is optimized considering robustness against weight variations and total ADC power consumption. Afterward, error compensation is introduced at necessary locations determined by reinforcement learning (RL) to rescue the feature maps with remaining errors. Experimental results demonstrate that inference accuracy of neural networks can be recovered from as low as 1.69% under variations back to more than 95% of their original accuracy at the highest level of variations and reducing total ADC power consumption by 55% while the training and hardware cost are negligible.

Deep neural networks (DNNs) have been successfully applied in various fields. A major challenge of deploying DNNs, especially on edge devices, is power consumption, due to the large number of multiply-and-accumulate (MAC) operations. To address this challenge, we propose PowerPruning, a novel method to reduce power consumption in digital neural network accelerators by selecting weights that lead to less power consumption in MAC operations. In addition, the timing characteristics of the selected weights together with all activation transitions are evaluated. The weights and activations that lead to small delays are further selected. Consequently, the maximum delay of the sensitized circuit paths in the MAC units is reduced even without modifying MAC units, which thus allows a flexible scaling of supply voltage to reduce power consumption further. Together with retraining, the proposed method can reduce power consumption of DNNs on hardware by up to 73.9% with only a slight accuracy loss.

In-memory-computing (IMC) has become an efficient solution for implementing neural networks on hardware. However, IMC platforms request weights in neural networks to be programmed to exact values. This is a very demanding task due to programming complexity, process variations, noise, as well as thermal effects. Accordingly, new methods should be introduced to counter such uncertainties. In this paper, we first discuss a method to train neural networks statistically with process variations modeled as correlated random variables. The statistical effect is incorporated in the cost function during training. Consequently, a neural network after statistical training becomes robust to uncertainties. To deal with variations and noise further, we also introduce a compensation method with extra layers for neural networks. These extra layers are trained offline again after the weights in the original neural network are determined to enhance the inference accuracy. Finally, we will discuss a method for testing the effect of process variations in an optical acceleration platform for neural networks. This optical platform uses Mach-Zehnder Interferometers (MZIs) to implement the multiply-accumulate operations. However, trigonometric functions in the transformation matrix of an MZI make it very sensitive to process variations. To address this problem, we apply a recursive test procedure to determine the properties of MZIs inside an optical acceleration module, so that process variations can be compensated accordingly to maintain the inference accuracy of neural networks.

In deep neural networks (DNNs), there are a huge number of weights and multiply-and-accumulate (MAC) operations. Accordingly, it is challenging to apply DNNs on resource- constrained platforms, e.g., mobile phones. Quantization is a method to reduce the size and the computational complexity of DNNs. Existing quantization methods either require hardware overhead to achieve a non-uniform quantization or focus on model-wise and layer-wise uniform quantization, which are not as fine-grained as filter-wise quantization. In this paper, we propose a class-based quantization method to determine the minimum number of quantization bits for each filter or neuron in DNNs individually. In the proposed method, the importance score of each filter or neuron with respect to the number of classes in the dataset is first evaluated. The larger the score is, the more important the filter or neuron is and thus the larger the number of quantization bits should be. Afterwards, a search algorithm is adopted to exploit the different importance of filters and neurons to determine the number of quantization bits of each filter or neuron. Experimental results demonstrate that the proposed method can maintain the inference accuracy with low bit-width quantization. Given the same number of quantization bits, the proposed method can also achieve a better inference accuracy than the existing methods.

Deep neural networks (DNNs) have successfully been applied in many fields in the past decades. However, the in-creasing number of multiply-and-accumulate (MAC) operations in DNNs prevents their application in resource-constrained and resource-varying platforms, e.g., mobile phones and autonomous vehicles. In such platforms, neural networks need to provide ac-ceptable results quickly and the accuracy of the results should be able to be enhanced dynamically according to the computational resources available in the computing system. To address these challenges, we propose a design framework called SteppingNet. SteppingNet constructs a series of sub nets whose accuracy is incrementally enhanced as more MAC operations become avail-able. Therefore, this design allows a trade-off between accuracy and latency. In addition, the larger sub nets in SteppingNet are built upon smaller subnets, so that the results of the latter can directly be reused in the former without recomputation. This property allows SteppingNet to decide on-the-fly whether to enhance the inference accuracy by executing further MAC operations. Experimental results demonstrate that SteppingNet provides an effective incremental accuracy improvement and its inference accuracy consistently outperforms the state-of-the-art work under the same limit of computational resources.

ReRAM-based accelerators have emerged as promising accelerators for deep neural networks (DNNs). How-ever, programming every ReRAM cell to its corresponding conductance before inference can be time-consuming and energy-intensive using existing one-by-one/row-by-row programming mechanisms. Although a two-phase multi-row programming scheme has been proposed to enhance programming efficiency, there are situations where multiple rows cannot be programmed together and only row-by-row programming can be employed. Therefore, this paper proposes a new block-based programming architecture for ReRAM crossbars that enables precise control of wordline and bitline transistors. In addition, a block-based programming framework, including the approximation phase and the fine-tuning phase, along with a multi-line programming algorithm and a programming-aware model retraining are proposed to reduce programming cycles and energy consumption. Experimental results demonstrate that our proposed method can reduce programming cycles and energy consumption by 46%-49 % and 63 % -64 %, respectively, compared to the state of the art. Additionally, the area and power overhead are negligible.

Optical neural networks with Mach-Zender interferometers (MZIs) have demonstrated advantages over their electronic counterparts in computing efficiency and power consumption. However, implementing the computation with a weight matrix in DNNs using this technique requires the decomposition of the weight matrix into two unitary matrices, because an optical network can only realize a single unitary matrix due to its structural property. Accordingly, a direct implementation of DNNs onto optical networks suffer from a low area efficiency. To address this challenge, in this paper, a near-unitary training framework is proposed. In this framework, a weight matrix in DNNs is first partitioned into square submatrices to reduce the number of MZIs in the optical networks. Afterwards, training is adjusted to make the partitioned submatrices as close to unitary as possible. Such a matrix is then represented further by the sum of a unitary matrix and a sparse matrix. The latter implements the difference between the unitary matrix and the near-unitary matrix after training. In this way, only one optical network is needed to implement this unitary matrix and the low computation load in the sparse matrix can be implemented with area-efficient microring resonators (MRRs). Experimental results show that the area footprint can be reduced by 81.81%, 85.51%, 48.6% for ResNet34, VGG16, and fully connected neural networks, respectively, while the inference accuracy is still maintained on CIFAR100 and MNIST datasets.

Memristor crossbar arrays are considered to be a promising platform for neuromorphic computing. To deploy a trained neural network (NN) model on memristor crossbars, memristors need to be programmed to the corresponding weight values. In fact, due to device-based process variation and noise, deviations of the stored weights from the trained weights are inevitable, thereby causing the degradation of the actual inference performance. This article proposes a unified Bayesian inference-based framework, BRoCoM, which connects device nonidealities and algorithmic training together for robust computing on memristor crossbars. BRoCoM is able to incorporate different levels of nonidealities into prior weight distribution, and transform robustness optimization to Bayesian NN (BNN) training, the weights of NNs are optimized to accommodate uncertainties and minimize inference degradation. Experimental results confirm the capability of the proposed BRoCoM to achieve stable inference performance while tolerating the nonideal effects of process variation and noise.

A fast and efficient search function across the database has been a core component for a number of data-intensive tasks in machine learning, IoT applications, and inference. However, the conventional digital machines implementing the search functionality with repetitive arithmetic operations suffer from the energy efficiency and performance degradation due to the significant data transfer between the storage and processing units in the Von Neumann architecture. Ternary content addressable memories (TCAMs) are an essential hardware form of computing-in-memory (CiM) designs that aim to overcome the data transfer bottlenecks by implementing the parallel associative search function within the memory blocks. While most state-of-the-art TCAM designs focus on improving the information density by harnessing compact nonvolatile memories (NVMs), little efforts have been spent on optimizing the energy efficiency of the NVM-based TCAM. In this article, by exploiting the ferroelectric FET (FeFET) as a representative NVM, we propose an NOR-type 2FeFET-1T and an NAND-type 2FeFET-2T TCAM designs that enable highly energy-efficient associative search by reducing the associated precharge overheads. We then propose a hybrid ferroelectric NAND-NOR (HFNN) TCAM design to further improve the energy efficiency. An HFNN-based segmented architecture is proposed to reduce the search delay and energy by search operation pipeline. Evaluation results suggest that the proposed 2FeFET-1T, 2FeFET-2T and HFNN TCAM design consume$3.03\times $,$8.08\times $, and$226.92\times $less search energy than the conventional 16T complementary metal oxide semiconductor (CMOS) TCAM, respectively. Application benchmarking shows that our proposed 2FeFET-1T/2FeFET-2T/HFNN TCAM can save, on average, 45.2%/50.6%/57.5% the GPU energy consumption as compared to the conventional GPU.

Content Addressable Memory (CAM) is widely used for associative search tasks in advanced machine learning models and data-intensive applications due to the highly parallel pattern matching capability. Most state-of-the-art CAM designs focus on reducing the CAM cell area by exploiting the nonvolatile memories (NVMs). There exists only little research on optimizing the design and energy efficiency of NVM based CAMs for practical deployment in edge devices and AI hardware. In this paper, we propose a general compact and energy efficient CAM design scheme that alleviates the design overhead by employing just one NVM device in the cell. We also propose an adaptive matchline (ML) precharge and discharge scheme that further optimizes the search energy by fully reducing the ML voltage swing. We consider Ferroelectric field effect transistors (FeFETs) as the representative NVM, and present a 2T-1FeFET CAM array including a sense amplifier implementing the proposed ML scheme. Evaluation results suggest that our proposed 2T-1FeFET CAM design achieves 6.64×/4.74×/9.14×/3.02× better energy efficiency compared with CMOS/ReRAM/STT-MRAM/2FeFET CAM arrays. Benchmarking results show that our approach provides 3.3×/2.1× energy-delay product improvement over the 2T-2R/2FeFET CAM in accelerating query processing applications.

Complex event-driven neuron dynamics was an obstacle to implementing efficient brain-inspired computing architectures with VLSI circuits. To solve this problem and harness the event-driven advantage, we propose ASTERS, a resistive random-access memory (ReRAM) based neuromorphic design to conduct the time-to-first-spike SNN inference. In addition to the fundamental novel axon and neuron circuits, we also propose two techniques through hardware-software co-design: "Multi-Level Firing Threshold Adjustment" to mitigate the impact of ReRAM device process variations, and "Timing Threshold Adjustment" to further speed up the computation. Experimental results show that our cross-layer solution ASTERS achieves more than 34.7% energy savings compared to the existing spiking neuromorphic designs, meanwhile maintaining 90.1% accuracy under the process variations with a 20% standard deviation.

Memristor-based crossbars, which can achieve 1-2 orders of magnitude energy efficiency improvement over digital machines, have been introduced to accelerate the neural networks of machine learning tasks. Due to the high voltage pulses repeatedly applied onto memristors during programming and online tuning, the effective resistance ranges of the memristors actually decrease as a result of aging, which eventually impair the inference accuracy of the neural network running on the memristor-based crossbar. In this paper, we propose an algorithm-hardware co-design framework combining aging aware retraining and gradient sparsification to mitigate the impact of aging and extend the lifetime of the crossbar. Experimental results show that the proposed method can effectively increase the inference accuracy by up to 16% even with severe aging, while the crossbar lifetime can be extended by up to $2.7\times$.

System-architecture design optimization of flow-based microfluidic biochips has been extensively investigated over the past decade. Most of the prior work, however, is still based on chip architectures with dedicated storage units and this, not only limits the performance of biochips, but also increases their fabrication cost. To overcome this limitation, a distributed channel-storage architecture can be implemented, where fluid samples can be cached temporarily in flow channels instead of using a dedicated storage. This new concept of fluid storage, however, requires a careful arrangement of fluid samples to enable the channels to fulfill the dual functions of transportation and caching. Moreover, to avoid cross-contamination between different fluidic flows, wash operations are necessary to remove the residue left in flow channels. In this article, we formulate the first practical system level design and wash optimization problem for microfluidic biochips with distributed channel storage architecture, considering high-level synthesis, physical design, and wash optimization simultaneously, and present a top-down design flow to solve this problem systematically. Given the protocol of a biochemical application and the corresponding design requirements, our goal is to generate a chip architecture with low fabrication cost. Meanwhile the biochemical application can be executed efficiently with an optimized wash scheme. Experimental results on multiple benchmarks confirm that our approach leads to short completion time of biochemical applications, low chip cost, as well as high wash efficiency.

Recent advances in continuous-flow microfluidics have enabled highly integrated lab-on-a-chip biochips. These chips can execute complex biochemical applications precisely and efficiently within a tiny area, but they require a large number of control ports and the corresponding control logic to generate required pressure patterns for flow control, which, consequently, offset their advantages and prevent their wide adoption. In this article, we propose the first flow-control layer co-synthesis flow called MiniControl, for continuous-flow microfluidic biochips under strict constraints for control ports, incorporating high-level synthesis, physical design, and control system design simultaneously, which has never been considered in previous work. With the maximum number of allowed control ports specified in advance, this synthesis flow aims to generate biochip architectures with high execution efficiency and the corresponding control systems with optimized timing performance. Besides, the overall cost of a biochip can be reduced and the tradeoff between a control system and execution efficiency of biochemical applications can be evaluated for the first time. The experimental results demonstrate that MiniControl leads to high execution efficiency, low platform cost, as well as excellent timing performance, while strictly satisfying the given control-port constraints.

Continuous-flow microfluidic biochips have attracted high research interest over the past years. Inside such a chip, fluid samples of milliliter volumes are efficiently transported between devices (e.g., mixers, heaters, etc.) to automatically perform various laboratory procedures in biology and biochemistry. Each transportation task, however, requires an exclusive flow path composed of multiple contiguous microchannels during its execution period. Excess/waste fluids, in the meantime, should be discarded by independent flow paths connected to waste ports. All these paths are etched in a very tiny chip area using multilayer soft lithography and driven by flow ports connecting with external pressure sources, forming a highly integrated chip architecture that determines the final performance of biochips. In this article, we propose a new and practical design flow called PathDriver+ (PD+) for the architecture design of microfluidic biochips, integrating the actual fluid manipulations into both high-level synthesis and physical design, which has never been considered in prior work. With this design flow, highly efficient chip architectures with a flow-path network that enables the actual fluid transportation and removal can be constructed automatically. Meanwhile, fluid volume management between devices and flow-path minimization are realized for the first time, thus, ensuring the correctness of assay outcomes while reducing the complexity of chip architectures. Additionally, diagonal channel routing is implemented to fundamentally improve the chip performance. The tradeoff between the numbers of channel intersections and fluidic ports is evaluated to further reduce the fabrication cost of biochips. The experimental results on multiple benchmarks confirm that the proposed design flow leads to high assay execution efficiency and low overall chip cost.

Programmable microfluidic devices (PMDs) have emerged as a new software-controlled architecture for next-generation flow-based biochips. These devices can be dynamically reconfigured to perform different bioassays flexibly and efficiently owing to their 2-D regularly arranged valve structure. However, PMDs are confronted with critical contamination issues due to the matrix-like structure with intersecting channels. In this article, a block-flushing method is proposed for contamination removal, based on which an overall contamination-aware synthesis flow is proposed. In the proposed block-flushing approach, contaminated areas are first collected according to specific patterns and then flushed as a whole to increase washing efficiency. Then, the synthesis flow integrating the block-flushing method is further optimized such that functional bioassay operations and washing operations can be performed simultaneously for higher efficiency. Experimental results demonstrate that the proposed washing approach reduces the washing time by 28% on commonly used bioassays. Equipped with the proposed washing method, our contamination-aware synthesis flow effectively reduces 30% of the completion time of the bioassays compared with the baseline method.

In digital circuit designs, sequential components such as flip-flops are used to synchronize signal propagations. Logic computations are aligned at and thus isolated by flip-flop stages. Although this fully synchronous style can reduce design efforts significantly, it may affect circuit performance negatively, because sequential components can only introduce delays into signal propagations but never accelerate them. In this article, we propose a new timing model, VirtualSync+, in which signals, specially those along critical paths, are allowed to propagate through several sequential stages without flip-flops. Timing constraints are still satisfied at the boundary of the optimized circuit to maintain a consistent interface with existing designs. By removing clock-to-q delays and setup time requirements of flip-flops on critical paths, the performance of a circuit can be pushed even beyond the limit of traditional sequential designs. In addition, we further enhance the optimization with VirtualSync+ by fine-tuning with commercial design tools, e.g., design compiler from Synopsys, to achieve more accurate result. To achieve this fine-tuning, we first optimize the circuits by reallocating sequential components with sequential and combinational components as delay units. Afterward, the removal locations of flip-flops with respect to the circuits under optimization are extracted and the corresponding wave-pipelining timing constraints compatible with commercial design tools are established. These timing constraints are then incorporated into the optimization flow of commercial tools to generate the optimized circuits. The experimental results demonstrate that circuit performance can be improved by up to 4% (average 1.5%) compared with that after extreme retiming and sizing, while the increase of area is still negligible. This timing performance is enhanced beyond the limit of traditional sequential designs. It also demonstrates that compared with those after retiming and sizing, the circuits with VirtualSync+ can achieve better timing performance under the same area cost or smaller area cost under the same clock period, respectively.

Paper-based digital microfluidic biochips (P-DMFBs) have recently attracted great attention for its low-cost, in-place, and fast fabrication. This technology is essential for agile bio-assay development and deployment. P-DMFBs print electrodes and associate control lines on paper to control droplets and complete bio-assays. However, P-DMFBs have following issues: 1) control line interference may cause unwanted droplet movements, 2) avoiding control interference degrades assay performance and routability, 3) single layer fabrication limits routability, and 4) expensive ink cost limits low-cost benefits of P-DMFBs. To solve above issues, this work proposes an interference-free design methodology to design P-DMFBs with fast assay speed, better routability, and compact printing area. The contributions are as follows: First, we categorize control interference into soft and hard. Second, we identify only soft interference happens and propose to remove soft control interference constraints. Third, we propose an interference-free design methodology. Finally, we propose a cost-efficient ILP-based fluidic design module. Experimental results show proposed method outperforms prior work [14] across all bio-assay benchmarks. Compared to previous work, our cost-optimized designs use only 47%~78% area, gain 3.6%~16.2% more routing resources, and achieve 0.97x~1.5x shorter assay completion time. Our performance-optimized designs can accelerate assay speed by 1.05x~1.65x using 81%~96% printed area.

RRAM-based crossbars and optical neural networks are attractive platforms to accelerate neuromorphic computing. However, both accelerators suffer from hardware uncertainties such as process variations. These uncertainty issues left unaddressed, the inference accuracy of these computing platforms can degrade significantly. In this paper, a statistical training method where weights under process variations and noise are modeled as statistical random variables is presented. To incorporate these statistical weights into training, the computations in neural networks are modified accordingly. For optical neural networks, we modify the cost function during software training to reduce the effects of process variations and thermal imbalance. In addition, the residual effects of process variations are extracted and calibrated in hardware test, and thermal variations on devices are also compensated in advance. Simulation results demonstrate that the inference accuracy can be improved significantly under hardware uncertainties for both platforms.

Memristor based crossbars are a promising platform for neural network acceleration. To deploy a trained network model on a memristor crossbar, memristors need to be programmed to realize the trained weights of the network. However, due to process and dynamic variations, deviation of weights from the trained value is inevitable and inference accuracy thus degrades. In this paper, we propose a unified Bayesian inference based framework which connects hardware variations and algorithmic training together for robust computing on memristor crossbars. The framework incorporates different levels of variations into priori weight distribution, and transforms robustness optimization to Bayesian neural network training, where weights of neural networks are optimized to accommodate variations and minimize inference degradation. Simulation results with the proposed framework confirm stable inference accuracy under process and dynamic variations.

Ternary content addressable memories (TCAMs) are a special form of computing-in-memory (CiM) circuits that aim to address the so-called memory wall issues by merging the parallel search function with memory blocks. Due to the content addressing nature, TCAMs have been widely utilized for search intensive tasks in low-power, data analytic applications, such as IP routers, associative memories, and learning models. While most state-of-the-art TCAM designs focus on improving the TCAM density by harnessing compact nonvolatile memories (NVMs), little efforts have been spent on reducing and optimizing the energy consumption of the NVM based TCAM. In this paper, by exploiting the Ferroelectric FET (FeFET) as a representative NVM, we propose two compact and energy-aware designs of ferroelectric TCAMs for low power applications. We first introduce a novel 2FeFET based XOR-like gate structure that can also be adopted to other NVMs, and then leverage the structure to propose two TCAM designs that achieve high energy efficiency by either reducing the associated precharge overhead (2FeFET-1T cell), or eliminating the precharge phase typically required by TCAMs (2FeFET-2T cell). We evaluate and compare the designs w.r.t area, search energy and delay at array level with other existing designs, and benchmark the proposed TCAM designs in an associative memory based GPU architecture. The results suggest that the proposed 2FeFET-1T/2FeFET-2T TCAM design consumes 3.03X/8.08X less search energy than the conventional 16T CMOS TCAM, while the proposed design cell area is only 32.1%/39.3% of the latter. Compared with the state-of-the-art 2FeFET only TCAM array, our proposed designs still achieve 1.79X and 4.79X search energy reduction, respectively. Moreover, our proposed designs can achieve, on average, 45.2%/51.5% energy saving compared with the conventional GPU based architecture at the application level.

The last decade has witnessed the breakthrough of deep neural networks (DNNs) in various fields, e.g., image/speech recognition. With the increasing depth of DNNs, the number of multiply-accumulate operations (MAC) with weights explodes significantly, preventing their applications in resource-constrained platforms. The existing weight pruning method is considered to be an effective method to compress neural networks for acceleration. However, weights after pruning usually exhibit irregular patterns. Implementing MAC operations with such irregular weight patterns on hardware platforms with regular designs, e.g., GPUs and systolic arrays, might result in an underutilization of hardware resources. To utilize the hardware resource efficiently, in this paper, we propose a hardware-software codesign framework for acceleration on systolic arrays. First, weights after unstructured pruning are reorganized into a dense cluster. Second, various blocks are selected to cover the cluster seamlessly. To support the concurrent computations of such blocks on systolic arrays, a multiplexing technique and the corresponding systolic architecture is developed for various CNNs. The experimental results demonstrate that the performance of CNN inferences can be improved significantly without accuracy loss.

Memristor-based crossbars are considered to be promising candidates to accelerate vector-matrix computation in deep neural networks. Before being applied for inference, mem-ristors in the crossbars should be programmed to conductances corresponding to the network weights after software training. Existing programming methods, however, adjust conductances of memristors individually with many programming-reading cycles. In this paper, we propose an efficient programming framework for memristor crossbars, where the programming process is partitioned into the predictive phase and the fine-tuning phase. In the predictive phase, multiple memristors are programmed simultaneously with a memristor programming model and IR-drop estimation. To deal with the programming inaccuracy resulting from process variations, noise and IR-drop and move conductances to target values, memristors are fine-tuned afterwards to reach a specified programming accuracy. Simulation results demonstrate that the proposed method can reduce the number of programming-reading cycles by up to 94.77% and 90.61% compared to existing one-by-one and row-by-row programming methods, respectively.

The memristor crossbar provides a unique opportunity to develop a neuromorphic computing system (NCS) with high scalability and energy efficiency. However, the reliability issues that arise from the immature fabrication process and physical device limitations, i.e., variations and stuck-at-faults (SAF), dramatically prevent its wide application in practice. Specifically, variations make the programmed weights deviate from their expected values. On the other hand, defective mem-ristors cannot even represent the weights effectively. In this work, we propose a variation- and defect-aware framework to improve the reliability of memristor-based NCS while minimizing the inference performance loss. We propose to develop analytical weight models to characterize the non-ideal effects of variations and SAFs, which can then be incorporated into a Bayesian neural network as priori and constraint. We then convert the reliability improvement to the neural network training for optimal weights that can accommodate variations and defects across the chips, which does not require computation-intensive retraining or cost-expensive testing. Extensive experimental results with the proposed framework confirm its effective capability of improving the reliability of NCS, while significantly mitigating the inference accuracy degradation under even severe variations and SAFs.

Multiplications have been commonly conducted in quantized CNNs, filters, and reconfigurable cores, and so on, which are widely deployed in mobile and embedded applications. Most multipliers are designed to perform multiplications with symmetric bit-widths, i.e., n - by n -bit multiplication. Such features would cause extra area overhead and performance loss when m - by n -bit multiplications ( m > n ) are deployed in the same hardware design, resulting in inefficient multiplication operations. It is highly desired and challenging to propose a reconfigurable multiplier design to accommodate operands with both symmetric and asymmetric bit-widths. In this work, we propose a reconfigurable approximate multiplier to support multiplications at various precisions, i.e., bit-widths. Unlike prior works of approximate adders assuming a uniform weight distribution with bit-wise independence, scenarios like a quantized CNN may have a centralized weight distribution and hence follow a Gaussian-like distribution with correlated adjacent bits. Thus, a new block-based approximate adder is also proposed as part of the multiplier to ensure energy-efficient operation with an awareness of the bit-wise correlation. Our experimental results show that the proposed approximate adder significantly reduces the error rate by 76% to 98% over a state-of-the-art approximate adder for Gaussian-like distribution scenarios. Evaluation results show that the proposed multiplier is 19% faster and 22% more power saving than a Xilinx multiplier IP at the same bit precision and achieves a 23.94-dB peak signal-to-noise ratio, which is comparable to the accurate one of 24.10 dB when deployed in a Gaussian filter for image processing tasks.

Flow-based microfluidic biochips have attracted much attention in the EDA community due to their miniaturized size and execution efficiency. Previous research, however, still follows the traditional computing model with a dedicated storage unit, which actually becomes a bottleneck of the performance of biochips. In this article, we propose a distributed channel-storage architecture (DCSA) to cache fluid samples inside flow channels temporarily. Since distributed storage can be accessed more efficiently than a dedicated storage unit and channels can switch between the roles of transportation and storage easily, biochips with this architecture can achieve a higher execution efficiency even with fewer resources. Furthermore, we also address the flow-path planning that enables the manipulation of actual fluid transportation/caching on a chip. The simulation results confirm that the execution efficiency of a bioassay can be improved significantly, while the number of valves in the biochip can be reduced accordingly. Also, flow paths for transportation tasks can be constructed and planned automatically with minimum extra resources.

In this paper, we will cover several techniques that can enhance the resilience of timing of digital circuits. Using post-silicon tuning components, the clock arrival times at flip-flops can be modified after manufacturing to balance delays between flip-flops. The actual delay properties of flip-flops will be examined to exploit the natural flexibility of such components. Wave-pipelining paths spanning several flip-flop stages can be integrated into a synchronous design to improve the circuit performance and to reduce area. In addition, with this technique, it cannot be taken for granted anymore that all the combinational paths in a circuit work with respect to one clock period. Therefore, a netlist alone does not represent all the design information. This feature enables the potential to embed wave-pipelining paths into a circuit to increase the complexity of reverse engineering. In order to replicate a design, attackers therefore have to identify the locations of the wave-pipelining paths, in addition to the netlist extracted from reverse engineering. Therefore, the security of the circuit against counterfeiting can be improved.

Microfluidic fully programmable valve array (FPVA) biochips have emerged as general-purpose flow-based microfluidic lab-on-chips (LoCs). An FPVA supports highly re-configurable on-chip components (modules) in the two-dimensional grid-like structure controlled by some software programs, unlike application-specific flow-based LoCs. Fluids can be loaded into or washed from a cell with the help of flows from the inlet to outlet of an FPVA, whereas cell-to-cell transportation of discrete fluid segment(s) is not precisely possible. The simplest mixing module to realize on an FPVA-based LoC is a four-way mixer consisting of a 2 × 2 array of cells working as a ring-like mixer having four valves. In this paper, we propose a design automation method for sample preparation that finds suitable placements of mixing operations of a mixing tree using four-way mixers without requiring any transportation of fluid(s) between modules. We also propose a heuristic that modifies the mixing tree to reduce the sample preparation time. We have performed an extensive simulation and examined several parameters to determine the performance of the proposed solution.

Processing-in-memory engines have successfully been applied to accelerate deep neural networks. For improving computing efficiency, spiking-based designs are widely explored. However, spiking-based designs quantize inter-layer signals naturally, leading to performance loss. In addition, the spike mismatch effect makes digital processing necessary, impeding direct signal transfer between layers and thus resulting in longer latency. In this paper, we propose a novel neuron design based on pulse width modulation, avoiding the quantization step and bypassing spike mismatch via the continuous activation. The computation latency and circuit complexity can significantly be reduced due to the absence of quantization and digital processing steps, while keeping a competitive performance. Simulation results show that the proposed neuron design can achieve > 100× speedup compared with spiking-based designs. The area and power consumption can be reduced up to 74.87% and 25.63%.

Memristor-based crossbars are an attractive platform to accelerate neuromorphic computing. However, process variations during manufacturing and noise in memristors cause significant accuracy loss if not addressed. In this paper, we propose to model process variations and noise as correlated random variables and incorporate them into the cost function during training. Consequently, the weights after this statistical training become more robust and together with global variation compensation provide a stable inference accuracy. Simulation results demonstrate that the mean value and the standard deviation of the inference accuracy can be improved significantly, by even up to 54% and 31%, respectively, in a two-layer fully connected neural network.

RRAM-based crossbars are a promising hardware platform to accelerate computations in neural networks. Before such a crossbar can be used as an accelerator for neural networks, RRAM cells should be programmed to target resistances to represent weights in neural networks. However, this process degrades the valid range of the resistances of RRAM cells from the fresh state, called aging effect. Therefore, after a certain number of programming iterations, these RRAM cells cannot be programmed reliably anymore, affecting the classification accuracy of neural networks negatively. In addition, process variations during manufacturing and noise during programming of RRAM cells also lead to significant accuracy degradation. To solve the problems described above, in this paper, we introduce a software/hardware codesign framework to reduce the aging effect in RRAM crossbars. To counter process variations and noise, we first model them as random variables and then modify the computations in software training considering these variables. Simulation results show that the lifetime of RRAM crossbars can be extended by up to 11 times with the codesign framework and the mean value and the standard deviation of the inference accuracy under process variations and noise can be improved significantly.

Continuous-flow microfluidic biochips have attracted high research interest over the past years. Inside such a chip, fluid samples of milliliter volumes are efficiently transported between devices (e.g., mixers, etc.) to automatically perform various laboratory procedures in biology and biochemistry. Each transportation task, however, requires an exclusive flow path composed of multiple contiguous microchannels during its execution period. Excess/waste fluids, in the meantime, should be discarded by independent flow paths connected to waste ports. All these paths are etched in a very tiny chip area using multilayer soft lithography and driven by flow ports connecting with external pressure sources, forming a highly integrated chip architecture that dominates the performance of biochips. In this paper, we propose a practical synthesis flow called PathDriver for the design automation of microfluidic biochips, integrating the actual fluid manipulations into both high-level synthesis and physical design, which has never been considered in prior work. Given the protocols of biochemical applications, PathDriver aims to generate highly efficient chip architectures with a flow-path network that enables the manipulation of actual fluid transportation and removal. Additionally, fluid volume management between devices and flow-path minimization are realized for the first time, thus ensuring the correctness of assay outcomes while reducing the complexity of chip architectures. Experimental results on multiple benchmarks demonstrate the effectiveness of the proposed synthesis flow.

Optical neural networks (ONNs) have emerged as a promising high-performance computing platform to accelerate deep neural networks. In ONNs, phases of light are modulated through Mach-Zehnder Interferometers (MZIs), and MZIs are connected in a gridlike layout to implement multiply-accumulate operations. However, ONNs are very sensitive to process variations and thermal effects. This sensitivity leads to a significant degradation of inference accuracy of ONNs and thus renders them unusable in practice. In this paper, we propose a framework to calibrate process variations and counter thermal effects by power compensation. Experimental results demonstrate that the proposed framework can recover the inference accuracy under variations and thermal effects, e.g., from as low as 11.05% back to 74.11% for LeNet-5 on Cifar10, so that ONNs can achieve an inference accuracy similar to the accuracy after software training while providing their high bandwidth in neuromorphic computing.

Flow-based microfluidic biochips have become a promising platform for complex biochemical assays. As the integration of such chips is increasing, a flexible general reconfigurable platform, fully programmable valve array (FPVA), has emerged. Such a 2-D array comprises regularly arranged valves using which flow-networks with different geometry, size, and connectivity can be constructed dynamically. However, the test generation for such arrays becomes challenging due to the large number of potential flow-networks and transportation paths that can be configured on-chip. In this article, we propose a strategy to generate efficient test patterns for FPVAs based on the concepts of test paths and cuts. These patterns together can cover multiple faults in both flow and control layers. We also introduce the concept of test trees and multiple cuts for a test pattern to deal with faults in FPVAs with multiple ports. Moreover, the proposed method can be applied to generate test patterns for traditional flow-based biochips with predefined architectures. The simulation results demonstrate that defects in FPVAs can be detected reliably by a limited number of test patterns generated by the proposed method. For traditional biochips with predefined architectures, these patterns also exhibit an improved test efficiency.

Paper-based digital microfluidic biochips (P-DMFBs) have recently emerged as a promising low-cost and fast-responsive platform for biochemical assays. In P-DMFBs, electrodes and control lines are printed on a piece of photograph paper using an inkjet printer and carbon nanotubes (CNTs) conductive ink. Compared with traditional digital microfluidic biochips (DMFBs), P-DMFBs enjoy significant advantages, such as faster in-place fabrication with printer and ink, lower costs, and better disposability. Since electrodes and CNT control lines are printed on the same side of this paper, a critical design challenge for P-DMFB is to prevent control interference between moving droplets and the voltages on CNT control lines. Control interference may result in unexpected droplet movements and thus incorrect assay outputs. To address this design challenge, a control-fluidic codesign methodology is proposed in this paper, along with two demonstrative design flows integrating both fluidic design and control design, i.e., the droplet-oriented codesign flow and the electrode-oriented codesign flow. The droplet-oriented flow is suitable for designing biochips with sparse electrodes and relatively larger number of droplets, whereas the electrode-oriented flow is suitable for biochips with dense electrodes and smaller number of droplets. The computational simulation results of real-life bioassays demonstrate the effectiveness of the proposed codesign flows.

With recent advances in reverse engineering, attackers can reconstruct a netlist to counterfeit chips by opening the die and scanning all layers of authentic chips. This relatively easy counterfeiting is made possible by the use of the standard simple clocking scheme, where all combinational blocks function within one clock period, so that a netlist of combinational logic gates and flip-flops is sufficient to duplicate a design. In this article, we propose to invalidate the assumption that a netlist completely represents the function of a circuit with unconventional timing. With the introduced wave-pipelining (WP) paths, attackers have to capture gate and interconnect delays during reverse engineering, or to test a huge number of combinational paths to identify the WP paths. To hinder the test-based attack, we construct false paths with WP to increase the counterfeiting challenge. The experimental results confirm that WP true paths and false paths can be constructed in benchmark circuits successfully with only a negligible cost, thus thwarting the potential attack techniques.

Flow-based microfluidic biochips are one of the most promising platforms used in biochemical and pharmaceutical laboratories due to their high efficiency and low costs. Inside such a chip, fluids of nanoliter volumes are transported between devices for various operations, such as mixing and detection. The transportation channels and corresponding operation devices are controlled by microvalves driven by external pressure sources. Since assigning an independent pressure source to every microvalve would be impractical due to high costs and limited system dimensions, states of microvalves are switched by a control logic using time multiplexing. Existing control-logic designs, however, still switch only a single control channel per operation, leading to a low efficiency. In this article, we present the first automatic synthesis approach for a control logic that is able to switch multiple control channels simultaneously. Moreover, we propose the first fault-aware design in control logic by introducing backup control paths to maintain the correct function even when manufacturing defects occur. The construction of control logic is achieved by a highly efficient framework based on particle swarm optimization, Boolean logic simplification, grid routing, together with mixing multiplexing. The simulation results demonstrate that the proposed multichannel switching mechanism leads to fewer valve-switching times and lower total logic cost, while realizing fault tolerance for all control channels.

Recent advances in continuous-flow microfluidics have enabled highly integrated lab-on-a-chip biochips. These chips can execute complex biochemical applications precisely and efficiently within a tiny area, but they require a large number of control ports and the corresponding control logic to generate required pressure patterns for flow control, which, consequently, offset their advantages and prevent their wide adoption. In this paper, we propose the first synthesis flow called MiniControl, for continuous-flow microfluidic biochips (CFMBs) under strict constraints for control ports, incorporating high-level synthesis and physical design simultaneously, which has never been considered in previous work. With the maximum number of allowed control ports specified in advance, this synthesis flow generates a biochip architecture with high execution efficiency. Moreover, the overall cost of a CFMB can be reduced and the tradeoff between control logic and execution efficiency of biochemical applications can be evaluated for the first time. Experimental results demonstrate that MiniControl leads to high execution efficiency and low overall platform cost, while satisfying the given control port constraint strictly.

Programmable Microfluidic Devices (PMDs) have revolutionized the traditional biochemical experiment flow. Test algorithms for PMDs have recently been proposed. Test patterns can be generated algorithmically. But an algorithm for fault localization once some faults have been identified is not yet available. When testing a PMD, once a test pattern fails it is unknown where the stuck valve is located. The stuck valve can be any one valve out of many valves forming the test pattern. In this paper, we propose an effective algorithm for the localization of stuck-at-0 faults and stuck-at-1 faults in a PMD. The stuck valve is localized either exactly or within a very small set of candidate valves. Once the locations of faulty valves are known, it becomes possible to continue to use the PMD by resynthesizing the application.

Flow-based microfluidic biochips (FBMBs) have attracted much attention over the past decade. On such a micrometer-scale platform, various biochemical applications, also called bioas-says, can be processed concurrently and automatically. To improve execution efficiency and reduce fabrication cost, a distributed channel-storage architecture (DCSA) can be implemented on this platform, where fluid samples can be cached temporarily in flow channels close to components. Although DCSA can improve the execution efficiency of FBMBs significantly, it requires a careful arrangement of fluid samples to enable the channels to fulfill the dual functions of transportation and caching. In this paper, we formulate the first flow-layer physical design problem considering DCSA, and propose a top-down synthesis algorithm to generate efficient solutions considering execution efficiency, washing, and resource usage simultaneously. Experimental results demonstrate that the proposed algorithm leads to a shorter execution time, less flow-channel length, and a higher efficiency of on-chip resource utilization for biochemical applications compared with a direct approach to incorporate distributed storage into existing frameworks.

Programmable Microfluidic Devices (PMDs) have emerged as a new architecture for next-generation flow-based biochips. These devices can be dynamically reconfigured to execute different bioassays flexibly and efficiently owing to their two-dimensional regularly-arranged valve structure. During execution of a bioassay or between the execution of multiple bioassays, some areas on the PMD, however, become contaminated and must be cleaned by washing them with a buffer flow before they are reused. In this paper, we propose a novel block-based washing technique called block flushing. In this method, contaminated areas are first collected according to given patterns and flushed as a whole to increase washing efficiency. Simulation results show that with this technique the proposed method can achieve on average 28% improvement in reducing washing time compared with two other baseline solutions.

Memristor-based crossbars have been applied successfully to accelerate vector-matrix computations in deep neural networks. During the training process of neural networks, the conductances of the memristors in the crossbars must be updated repetitively. However, memristors can only be programmed reliably for a given number of times. Afterwards, the working ranges of the memristors deviate from the fresh state. As a result, the weights of the corresponding neural networks cannot be implemented correctly and the classification accuracy drops significantly. This phenomenon is called aging, and it limits the lifetime of memristor-based crossbars. In this paper, we propose a co-optimization framework combining software training and hardware mapping to reduce the aging effect. Experimental results demonstrate that the proposed framework can extend the lifetime of such crossbars up to 11 times, while the expected accuracy of classification is maintained.

At nanometer manufacturing technology nodes, process variations affect circuit performance significantly. This trend leads to a large timing margin and thus overdesign in the traditional worst-case circuit design flow. To combat this pessimism, post-silicon clock tuning buffers can be deployed to balance timing slacks of consecutive combinational paths in individual chips by tuning clock skews after manufacturing. A challenge of this method is that path delays of each chip with timing failures should be measured to gather the information for clock skew configuration. However, current methods for delay measurement rely on path-wise frequency stepping, which requires much time from expensive testers. In this paper, we propose an efficient delay test framework (EffiTest2) to solve the post-silicon testing problem by testing only representative paths with delay alignment using the already-existing tunable buffers in the circuit. Experimental results demonstrate that EffiTest2 can reduce the number of frequency stepping iterations by more than 94% with only a slight yield loss.

MEMS-based sensor circuits are traditionally designed separately using CAD tools specific to each energy domain (electrical and mechanical). This article presents a complete approach for combined MEMS-IC robustness optimization. Advanced methods for robustness analysis and optimization considering design, operating and process parameters, developed for integrated circuits, are transferred to MEMS-IC systems. Both electrical and mechanical design and process parameters are included in the optimization. The methodology is exemplified on two demonstrator examples: a MEMS microphone and a MEMS accelerometer, each with an integrated readout circuit. A successful optimization requires the simultaneous inclusion of design parameters and process tolerances from both energy domains. To save CPU time, a reduced-order, circuit-level model is used for the MEMS part and this model is created only when necessary. To integrate the generation of the simplified model into the optimization flow, a simulation-in-a-loop flow based on commercial tools for both the electrical and the mechanical domain has been implemented.

Optical networks-on-chips (ONoCs) have become a promising solution for the on-chip communication of multi-and many-core systems to provide superior communication bandwidths, efficiency in power consumption, and latency performance compared to electronic NoCs. Serving as the critical part of ONoCs, an optical router composed of waveguides and photonic switching elements (PSEs) routes signals between two hubs or between a hub and a memory controller. Many studies focus on developing efficient architectures of optical routers, while their physical implementation that can seriously deteriorate the quality of the architectures is rarely addressed. The existing automatic place-and-route tools suffer from considerable insertion loss due to many waveguide crossings outside of PSEs, which leads to huge power consumption of laser sources. By observing that the logic schemes of most optical routers are actually planar, we develop a concurrent PSE placement and waveguide routing flow, called PlanarONoC, that guarantees optimal solutions in terms of crossings for planar logic schemes. Experimental results show that the proposed flow reduces the maximum insertion loss by 37% on average, guarantees no waveguide crossing outside of PSEs, and performs much more efficient compared to the state-of-the-art work.

Flow-based microfluidic biochips are gaining traction in the microfluidics community since they enable efficient and low-cost biochemical experiments. These highly integrated lab-on-a-chip systems, however, suffer from manufacturing defects, which cause some chips to malfunction. To test biochips after manufacturing, air pressure is applied to input ports of a chip and predetermined test vectors are used to change the states of microvalves in the chip. Pressure meters are connected to the output ports to measure pressure values, which are compared with expected values to detect errors. To reduce the cost of the test platform, the number of pressure sources and meters should be reduced. We propose a design-for-testability (DFT) technique that enables a test procedure with only a single pressure source and a single pressure meter. Furthermore, the valves inserted for DFT share control channels with valves in the original chip so that no additional control signals are required. Simulation results demonstrate that this technique can generate efficient chip architectures for single-source single-meter test in all experiment cases successfully to reduce test cost, while the performance of these chips in executing applications is still maintained.

In digital circuit designs, sequential components such as flip-flops are used to synchronize signal propagations. Logic computations are aligned at and thus isolated by flip-flop stages. Although this fully synchronous style can reduce design efforts significantly, it may affect circuit performance negatively, because sequential components can only introduce delays into signal propagations instead of accelerating them. In this paper, we propose a new timing model, VirtualSync, in which signals, specially those along critical paths, are allowed to propagate through several sequential stages without flip-flops. Timing constraints are still satisfied at the boundary of the optimized circuit to maintain a consistent interface with existing designs. By removing clock-to-q delays and setup time requirements of lip-lops on critical paths, the performance of a circuit can be pushed even beyond the limit of traditional sequential designs. Experimental results demonstrate that circuit performance can be improved by up to 11.5% (average 3.1%) compared with that after thorough sizing and retiming, while the increase of area is still negligible.

With recent advances in reverse engineering, attackers can reconstruct a netlist to counterfeit chips by opening the die and scanning all layers of original chips. This relatively easy counterfeiting is made possible by the use of the standard simple clocking scheme where all combinational blocks function within one clock period. In this paper, we propose a method to invalidate the assumption that a netlist completely represents the function of a circuit. With the help of wave-pipelining paths, this method forces attackers to capture delay information from manufactured chips, which is a very challenging task because we also introduce false paths. Experimental results confirm that wave-pipelining paths and false paths can be constructed in benchmark circuits successfully with only a negligible cost, while the potential attack techniques can be thwarted.

This overview paper summarizes the content of a tutorial given at the 2018 edition of the Forum on specification & Design Languages. The aim of the tutorial was to introduce the technology of microfluidic devices, which gained significant interest in the recent past, as well as corresponding design challenges to a community focused on design automation and corresponding specification/design languages. By this, the overview presents a starting point for researchers and engineers interested in getting involved in this area.

Continuous flow-based biochips are one of the promising platforms used in biochemical and pharmaceutical laboratories due to their efficiency and low costs. Inside such a chip, fluid volumes of nanoliter size are transported between devices for various operations, such as mixing and detection. The transportation channels and corresponding operation devices are controlled by microvalves driven by external pressure sources. Since assigning an independent pressure source to every microvalve would be impractical due to high costs and limited system dimensions, states of microvalves are switched using a control logic by time multiplexing. Existing control logic designs, however, still switch only a single control channel per operation – leading to a low efficiency. In this paper, we propose the first automatic synthesis approach for a control logic that is able to switch multiple control channels simultaneously to reduce the overall switching time of valve states. In addition, we propose the first fault-aware design in control logic to introduce redundant control paths to maintain the correct function even when manufacturing defects occur. Compared with the existing direct connection method, the proposed multi-channel switching mechanism can reduce the switching time of valve states by up to 64%. In addition, all control paths for fault tolerance have been realized.

Many-core chip architectures integrate tens to hundreds of processor cores on a single chip. Recent development of photonic interconnects has made Optical Networks-on-Chips (ONoCs) an attractive technology to overcome the drawbacks of electrical networks-on-chips. With ultra-high bandwidth, low latency, and great energy efficiency, ONoCs enable the designer to build scalable systems. However, photonic devices are sensitive to temperature fluctuations, and hence, require proactive management. This paper first calculates the thermal distribution from cell distribution using an approximated Green's function and proposes a post-placement algorithm to reduce the number of photonic devices in the hotspots. The paper then improves the routing algorithm considering bending loss and temperature variations. Experimental results also verify the efficiency and effectiveness of our algorithm.

Continuous-flow microfluidic large-scale integration (mLSI) shows increasing importance in biological/chemical fields, thanks to its advantages in miniaturization and high throughput. Current mLSI is designed manually, which is time-consuming and error-prone. In recent years, design automation research for mLSI has evolved rapidly, aiming to replace manual labor by computers. However, previous design automation approaches used to design each microfluidic layer separately and over-simplify the layer interactions to various degrees, which resulted in a gap between realistic requirements and automatically generated designs. In this paper, we propose a module model library to accurately model microfluidic components involving layer interactions; and we propose a co-layout synthesis tool, Columba, which generates AutoCAD-compatible designs that fulfill all designs rules and can be directly used for mask fabrication. Columba takes plain-text netlist descriptions as inputs, and performs simultaneous placement and routing for multiple layers while ensuring the planarity of each layer. We validate Columba by fabricating two of its output designs. Columba is the first design automation tool that can seamlessly synchronize with the manufacturing flow.

At submicrometer manufacturing technology nodes, process variations affect circuit performance significantly. To counter these variations, engineers are reserving more timing margin to maintain yield, leading to an unaffordable overdesign. Most of these margins, however, are wasted after manufacturing, because process variations cause only some chips to be really slow, while other chips can easily meet given timing specifications. To reduce this pessimism, we can reserve less timing margin and tune failed chips after manufacturing with clock buffers to make them meet timing specifications. With this post-silicon clock tuning, critical paths can be balanced with neighboring paths in each chip specifically to counter the effect of process variations. Consequently, chips with timing failures can be rescued and the yield can thus be improved. This is specially useful in high-performance designs, e.g., high-end CPUs, where clock binning makes chips with higher performance much more profitable. In this paper, we propose a method to determine where to insert post-silicon tuning buffers during the design phase to improve the overall profit with clock binning. This method learns the buffer locations with a Sobol sequence iteratively and reduces the buffer ranges afterward with tuning concentration and buffer grouping. Experimental results demonstrate that the proposed method can achieve a profit improvement of about 14% on average and up to 26%, with only a small number of tuning buffers inserted into the circuit.

Flow-based microfluidic biochips have progressed significantly in the past decade. Thanks to innovations in multilayer soft lithography (MSL) fabrication technology, the integration of thousands of microvalves along with large-scale networks of microchannels on a chip has been enabled. This progress has even been compared to the evolution of VLSI circuits following Moore's Law. In flow-based microfluidic biochips, microvalves are critical components to control the fluidic transportation for complex operations. To activate the open/close states of a microvalve, off-chip control pins are required. Due to the tremendous increase of the number of microvalves, a software-programmable microfluidic platform has been proposed to reduce the number of off-chip control pins, which integrates a microfluidic multiplexer on a separate control layer to control the array of microvalves. The multiplexer needs to be switched when the states of microvalves are changed between every two adjacent time slots. High switching frequency will make the multiplexer vulnerable and decrease the chip's reliability. We observe that different switching orders of microvalves lead to different switching frequencies of a multiplexer. Based on this observation, this paper proposes the first Hamming-distance-based switching order optimization method for microvalves to enhance the reliability of the multiplexer. Experimental results show that our method can significantly reduce the switching frequency of multiplexer, and the solution is very close to the theoretical optimal lower bound.

Technological innovations in continuous-flow microfluidics require updated automated synthesis methods. As new microfluidic components and biochemical applications are constantly introduced, the current functionality-based application mapping methods and the fixed-time-slot scheduling methods are insufficient to solve the new design challenges. In this work, we propose a component-oriented general device concept that enables precise description of operations and devices, and adapts well to technological updates. Applying this concept, we propose a layering algorithm together with a mathematical modeling method to synthesize binding and hybrid-scheduling solutions that support both fixed schedule and real-time decisions. We also consider potential chip layout and optimize the number of flow channels among devices to save routing efforts. Experimental results demonstrate that our solution fully utilizes the chip resources and can handle operations with different requirements.

Flow-based microfluidic biochips have attracted much attention in the EDA community due to their miniaturized size and execution efficiency. Previous research, however, still follows the traditional computing model with a dedicated storage unit, which actually becomes a bottleneck of the performance of biochips. In this paper, we propose the first architectural synthesis framework considering distributed storage constructed temporarily from transportation channels to cache fluid samples. Since distributed storage can be accessed more efficiently than a dedicated storage unit and channels can switch between the roles of transportation and storage easily, biochips with this distributed computing architecture can achieve a higher execution efficiency even with fewer resources. Experimental results confirm that the execution efficiency of a bioassay can be improved by up to 28% while the number of valves in the biochip can be reduced effectively.

Fully Programmable Valve Array (FPVA) has emerged as a new architecture for the next-generation flow-based microfluidic biochips. This 2D-array consists of regularly-arranged valves, which can be dynamically configured by users to realize microfluidic devices of different shapes and sizes as well as interconnections. Additionally, the regularity of the underlying structure renders FPVAs easier to integrate on a tiny chip. However, these arrays may suffer from various manufacturing defects such as blockage and leakage in control and flow channels. Unfortunately, no efficient method is yet known for testing such a general-purpose architecture. In this paper, we present a novel formulation using the concept of flow paths and cut-sets, and describe an ILP-based hierarchical strategy for generating compact test sets that can detect multiple faults in FPVAs. Simulation results demonstrate the efficacy of the proposed method in detecting manufacturing faults with only a small number of test vectors.

With advancing process technologies and booming Internet of Things markets, millimeter-wave CMOS RFICs have evolved rapidly and been widely applied in recent years. The performance of CMOS RFICs is very sensitive to the chip layout, and a tiny variation of the microstrip length can cause a large impact to the circuit performance. This results in a time-consuming tuning process including much simulation effort for chip design, which becomes the major bottleneck for time to market. This paper introduces a progressive integer-linear-programming-based method consisting of two phases: 1) global layout generation and 2) iterative validation. In the global layout generation phase, we focus on the most critical constraints such as layout planarity and device connection relations to determine the topology of the final design. This provides a basis for constructing the accurate model in the iterative validation phase. The layouts generated by applying our method can satisfy very stringent routing requirements of microstrip lines, including spacing/noncrossing rules, precise length, and bend number minimization, within a given layout area. The resulting RFIC layouts excel in both performance and area with much fewer bends compared with the simulation-tuning based manual layout, while the layout generation time is significantly reduced from weeks to a few minutes.

At nanometer manufacturing technology nodes, process variations affect circuit performance significantly. In addition, performance deterioration of circuits due to aging effects is also increasing. Consequently, a large timing margin is required to maintain yield. To combat the pessimism and the resulting overdesign, aging analysis with highlevel models, on-chip timing margin monitoring and tuning, and flexible delay models of flip-flops can be deployed. This paper gives an overview of the state of the art of applying these techniques to improve the health of circuits.

Continuous-flow microfluidics have evolved rapidly in the last decades, due to their advantages in effective and accurate control. However, complex control results in complicated valve actuations. As a result, sophisticated interactions between control and flow layers substantially raise the design difficulty. Previous work on design automation for microfluidics neglects the interactions between the control and flow layers and designs each layer separately, which leads to unrealistic designs. We propose the first planarity-guaranteed architectural model, and the first physical-design module models for important microfluidic components, which have modelled the interactions between both control and flow layers, while reducing the design difficulty. Based on the above, we propose the co-layout synthesis tool called Columba, which considers the pressure sharing among different valves, and routes channels in an any-angled manner. Experimental results show that complicated designs considering layer interactions can be synthesized for the first time.

With advancing process technologies and booming IoT markets, millimeter-wave CMOS RFICs have been widely developed in recent years. Since the performance of CMOS RFICs is very sensitive to the precision of the layout, precise placement of devices and precisely matched microstrip lengths to given values have been a labor-intensive and time-consuming task, and thus become a major bottleneck for time to market. This paper introduces a progressive integer-linear-programming-based method to generate high-quality RFIC layouts satisfying very stringent routing requirements of microstrip lines, including spacing/non-crossing rules, precise length, and bend number minimization, within a given layout area. The resulting RFIC layouts excel in both performance and area with much fewer bends compared with the simulation-tuning based manual layout, while the layout generation time is significantly reduced from weeks to half an hour.

At nanometer manufacturing technology nodes, process variations significantly affect circuit performance. To combat them, post-silicon clock tuning buffers can be deployed to balance timing budgets of critical paths for each individual chip after manufacturing. The challenge of this method is that path delays should be measured for each chip to configure the tuning buffers properly. Current methods for this delay measurement rely on path-wise frequency stepping. This strategy, however, requires too much time from expensive testers. In this paper, we propose an efficient delay test framework (EffiTest) to solve the post-silicon testing problem by aligning path delays using the already-existing tuning buffers in the circuit. In addition, we only test representative paths and the delays of other paths are estimated by statistical delay prediction. Experimental results demonstrate that the proposed method can reduce the number of frequency stepping iterations by more than 94% with only a slight yield loss.

Paper-based digital microfluidic biochips (P-DMFBs) have recently emerged as a promising low-cost and fast-responsive platform for biochemical assays. In P-DMFBs, electrodes and control lines are printed on a piece of photo paper using inkjet printer and conductive ink of carbon nanotubes (CNTs). Compared with traditional digital microfluidic biochips (DMFBs), P-DMFBs enjoy notable advantages, such as faster in-place fabrication with printer and ink, lower costs, better disposability, etc. Because electrodes and CNT control lines are printed on the same side of a paper, a new design challenge for P-DMFB is to prevent the interference between moving droplets and the voltages on CNT control lines. These interactions may result in unexpected droplet movements and thus incorrect assay outputs. To address the new challenges in automated design of P-DMFBs, this paper proposes the first control-fluidic codesign flow, which simultaneously adjusts the control line routing and fluidic droplet scheduling to achieve an optimized solution. As the control line routing may not be able to address all the interferences between moving droplets and the voltages on control lines, droplet rescheduling is performed to effectively deal with the remaining interferences in the routing solution. Computational simulation results on real-life bioassays show that the proposed codesign method successfully eliminates all the interferences, while a state-of-the-art maze routing method cannot solve any of the benchmarks without conflicts.

While previous decades have witnessed impressive accomplishments in the design and realization of conventional computing devices, physical boundaries and cost restrictions led to an increasing interest in alternative technologies (often referred to as Beyond CMOS or More than Moore technologies). In addition, these accomplishments also triggered many “complementary” applications and led to technologies providing an additional value to the conventional logic (often referred to as More than Moore). This led to a variety of emerging technologies such as Quantum Computation, Optical Circuits, or Microfluidic Biochips out of which many are considered very promising and some even entered the market recently. This poses new challenges to researchers and engineers working in computer-aided design. In this tutorial paper, we provide an overview on the main concepts of selected emerging technologies as well as the resulting design methods. To this end, we review the respective technological background and introduce the correspondingly used circuit models. Based on that, we show how computer-aided design has to adapt the common design tasks and review recently proposed solutions.

In static timing analysis, clock-to-q delays of flip-flops are considered as constants. Setup times and hold times are characterized separately and also used as constants. The characterized delays, setup times and hold times, are applied in timing analysis independently to verify the performance of circuits. In reality, however, clock-to-q delays of flip-flops depend on both setup and hold times. Instead of being constants, these delays change with respect to different setup/hold time combinations. Consequently, the simple abstraction of setup/hold times and constant clock-to-q delays introduces inaccuracy in timing analysis. In this paper, we propose a holistic method to consider the relation between clock-to-q delays and setup/hold time combinations with a piecewise linear model. The result is more accurate than that of traditional timing analysis, and the incorporation of the interdependency between clock-to-q delays, setup times and hold times may also improve circuit performance.

In flow-based biochips, peristaltic pumps consisting of valves are essential to generate circulation flow in a mixer. When a peristaltic pump is activated, the related valves for peristalsis are required to be actuated for many times. However, the roles of valves in traditional chips are fixed, and therefore the valves for peristalsis can wear out much faster than the valves for guiding fluid transportation. This could lead to a reduced lifetime of the chip, because the whole chip function can be affected when just a few or even only a single valve wears out. In this paper, we propose a valve-centered architecture with virtual valves, based on which we introduce a valve-role-changing concept to balance the valve actuations. By switching a valve into different roles, microfluidic components such as mixers, storages, and flow channels can be formed dynamically during the assay process, which enables us to balance the utilization of valves, and synthesize designs that support various kinds of operations. Compared with our preliminary work, we further decrease the largest number of valve actuation as well as the number of valves by the revised dynamic device mapping and fluid path routing. For dynamic device mapping, we introduce a virtual-boundary concept to generate devices at better places while connections between devices are still guaranteed. For fluid path routing, we accurately model valve actuation resulting from our valve-actuation-aware routing, and revise the results by rip-up and reroute. In addition to performance, we improve the reliability of our method by assuring fluid paths from devices to chip boundaries. Experiments show that the new method can be eight times better than the traditional method, and outperforms our preliminary work for large cases even with fewer valves.

On flow-based biochips, valves that are used to form peristaltic pumps wear out much earlier than valves for transportation since the former are actuated more often, which leads to a reduced lifetime of the chip. In this paper, we introduce a valve-role-changing concept to avoid always using the same valves for peristalsis. Based on this, we generate dynamic devices from a valve-centered architecture to distribute the valve actuation activities evenly and reduce the largest number of valve actuations with even fewer valves. In addition, we propose in situ on-chip storages, which can overlap with other devices, so that less area is needed compared with dedicated storages on traditional chips. Moreover, our method provides good support for assays requiring different volumes and ratios of samples. Experiments show that compared with traditional designs, the largest number of valve actuations can be reduced by 72.97% averagely, while the number of valves is reduced by 10.62%.

Post-silicon clock tuning elements are widely used in high-performance designs to mitigate the effects of process variations and aging. Located on clock paths to flip-flops, these tuning elements can be configured through the scan chain so that clock skews to these flip-flops can be adjusted after manufacturing. Owing to the delay compensation across consecutive register stages enabled by the clock tuning elements, higher yield and enhanced robustness can be achieved. These benefits are, nonetheless, attained by increasing die area due to the inserted clock tuning elements. For balancing performance improvement and area cost, an efficient timing analysis algorithm is needed to evaluate the performance of such a circuit. So far this evaluation is only possible by Monte Carlo simulation which is very time-consuming. In this paper, we propose an alternative method using graph transformation, which computes a parametric minimum clock period and is more than 104times faster than Monte Carlo simulation while maintaining a good accuracy. This method also identifies the gates that are critical to circuit performance, so that a fast analysis-optimization flow becomes possible.

Length-matching is an important technique to balance delays of bus signals in high-performance printed circuit board (PCB) routing. Existing routers, however, may generate very dense meander segments. Signals propagating along these meander segments exhibit a speedup effect due to crosstalk between the segments of the same wire, thus leading to mismatch of arrival times even under the same physical wire length. In this paper, we present a post-processing method to enlarge the width and the distance of meander segments and hence distribute them more evenly on the board so that crosstalk can be reduced. In the proposed framework, we model the sharing of available routing areas after removing dense meander segments from the initial routing, as well as the generation of relaxed meander segments and their groups for wire length compensation. This model is transformed into an ILP problem and solved for a balanced distribution of wire patterns. In addition, we adjust the locations of long wire segments according to wire priorities to swap free spaces toward critical wires that need much length compensation. To reduce the problem space of the ILP model, we also introduce a progressive fixing technique so that wire patterns are grown gradually from the edge of the routing toward the center area. Experimental results show that the proposed method can expand meander segments significantly even under very tight area constraints, so that the speedup effect can be alleviated effectively in high-performance PCB designs.

In this paper, we propose a post-processing framework which iteratively refines the routing results from an existing PCB router by removing dense meander segments. By swapping and detouring dense meander segments the proposed method can effectively alleviate accumulating crosstalk noise, while respecting pre-defined area constraints. Experimental results show more than 85% reduction of the meander segments and hence the noise cost.

Length-matching is an important technique to balance delays of bus signals in high-performance PCB routing. Existing routers, however, may generate dense meander segments with small distance. Signals propagating across these meander segments exhibit a speedup effect due to crosstalks between the segments of the same wire, thus leading to mismatch of arrival times even with the same physical wire length. In this paper, we propose a post-processing method to enlarge the width and the distance of meander segments and distribute them more evenly on the board so that the crosstalks can be reduced. In the proposed framework, we model the sharing combinations of available routing areas after removing dense meander segments from the initial routing, as well as the generation of relaxed meander segments and their groups in subareas. Thereafter, this model is transformed into an ILP problem and solved efficiently. Experimental results show that the proposed method can extend the width and the distance of meander segments about two times even under very tight area constraints, so that the crosstalks and thus the speedup effect can be alleviated effectively in high-performance PCB designs.

In this paper, we investigate the challenges of applying statistical static timing analysis in hierarchical design flow, where modules supplied by IP vendors are used to hide design details for IP protection and to reduce the complexity of design and verification. For the three basic circuit types, combinational, flip-flop-based, and latch-controlled, we propose methods for extracting timing models that contain interfacing and compressed internal constraints. Using these compact timing models, the runtime of full-chip timing analysis can be reduced, while circuit details from IP vendors are not exposed. We also propose a method for reconstructing correlation between modules during full-chip timing analysis. This correlation cannot be incorporated into timing models because it depends on the layout of the corresponding modules in the chip. In addition, we investigate how to apply the extracted timing models with the reconstructed correlation to evaluate the performance of the complete design. Experiments demonstrate that using the extracted timing models and reconstructed correlation full-chip timing analysis can be several times faster than applying the flattened circuit directly, while the accuracy of statistical timing analysis is still well maintained.

System-level power analysis is commonly used in modern SoC design processes to evaluate power consumption at early design phases. With the increasing variations in manufacturing, the statistical characteristics of parameters are also incorporated in the state-of-the-art methods. However, the spatial correlation between modules still remains as a challenge for system-level statistical power analysis where power models generated from individual modules are used for analysis efficiency or IP protection. In this paper, we propose a novel method to extract variation-aware and correlation-inclusive leakage power models for fast and accurate system-level analysis. For each individual module we generate a power model with different correlation information specified by the module vendor or customer. The local random variables in the power models are replaced by the corresponding ones at system level to reconstruct the correlation between modules so that the accuracy of system-level analysis is guaranteed. Experimental results show that our method are very accurate while being 1000X faster than Monte Carlo simulation and 70X-100X faster than the flattened full chip statistical leakage analysis.

Level-sensitive latches are widely used in high-performance designs. For such circuits, efficient statistical timing analysis algorithms are needed to take increasing process variations into account. The existing methods for solving this problem are still computationally expensive and can only provide the yield at a given clock period. In this paper, we propose a method combining reduced iterations and graph transformations. The reduced iterations extract setup time constraints and identify a subgraph for the following graph transformations handling the constraints from nonpositive loops. The combined algorithms are very efficient, more than ten times faster than other existing methods, and result in a parametric minimum clock period, which, together with the hold-time constraints, can be used to compute the yield at any given clock period very easily.

Post-Silicon Tunable (PST) clock buffers are widely used in high performance designs to counter process variations. By allowing delay compensation between consecutive register stages, PST buffers can effectively improve the yield of digital circuits. To date, the evaluation of manufacturing yield in the presence of PST buffers is only possible using Monte Carlo simulation. In this paper, we propose an alternative method based on graph transformations, which is much faster, more than 1000 times, and computes a parametric minimum clock period. It also identifies the gates which are most critical to the circuit performance, therefore enabling a fast analysis-optimization flow.

Latch-controlled circuits have a remarkable advantage in timing performance as process variations become more relevant for circuit design. Existing methods of statistical timing analysis for such circuits, however, still need improvement in runtime and their results should be extended to provide yield information for any given clock period. In this paper, we propose a method combining a simplified iteration and a graph transformation algorithm. The result of this method is in a parametric form so that the yield for any given clock period can easily be evaluated. The graph transformation algorithm handles the constraints from nonpositive loops effectively, completely avoiding the heuristics used in other existing methods. Therefore the accuracy of the timing analysis is well maintained. Additionally, the proposed method is much faster than other existing methods. Especially for large circuits it offers about 100 times performance improvement in timing verification.

Statistical static timing analysis deals with the increasing variations in manufacturing processes to reduce the pessimism in the worst case timing analysis. Because of the correlation between delays of circuit components, timing model generation and hierarchical timing analysis face more challenges than in static timing analysis. In this paper, a novel method to generate timing models for combinational circuits considering variations is proposed. The resulting timing models have accurate input-output delays and are about 80% smaller than the original circuits. Additionally, an accurate hierarchical timing analysis method at design level using pre-characterized timing models is proposed. This method incorporates the correlation between modules by replacing independent random variables to improve timing accuracy. Experimental results show that the correlation between modules strongly affects the delay distribution of the hierarchical design and the proposed method has good accuracy compared with Monte Carlo simulation, but is faster by three orders of magnitude.

As semiconductor devices continue to scale down, process variations become more relevant for circuit design. Facing such variations, statistical static timing analysis is introduced to model variations more accurately so that the pessimism in traditional worst case timing analysis is reduced. Because all delays are modeled using correlated random variables, most statistical timing methods are much slower than corner based timing analysis. To speed up statistical timing analysis, we propose a method to extract timing models for flip-flop and latch based sequential circuits respectively. When such a circuit is used as a module in a hierarchical design, the timing model instead of the original circuit is used for timing analysis. The extracted timing models are much smaller than the original circuits. Experiments show that using extracted timing models accelerates timing verification by orders of magnitude compared to previous approaches using flat netlists directly. Accuracy is maintained, however, with the mean and standard deviation of the clock period both showing usually less than 1% error compared to Monte Carlo simulation on a number of benchmark circuits.