Emerging data-driven applications in the embedded, e-Health, and internet of things (IoT) domain require complex on-device signal analysis and data reduction to maximize energy efficiency on these energy-constrained devices. Coarse-grained reconfigurable architectures (CGRAs) have been proposed as a good compromise between flexibility and energy efficiency for ultra-low power (ULP) signal processing. Existing CGRAs are often specialized and domain-specific or can only accelerate simple kernels, which makes accelerating complete applications on a CGRA while maintaining high energy efficiency an open issue. Moreover, the lack of instruction set architecture (ISA) standardization across CGRAs makes code generation using current compiler technology a major challenge. This work introduces R-Blocks; a ULP CGRA with HW/SW co-design tool-flow based on the OpenASIP toolset. This CGRA is extremely flexible due to its well-established VLIW-SIMD execution model and support for flexible SIMD-processing, while maintaining an extremely high energy efficiency using software bypassing, optimized instruction delivery, and local scratchpad memories. R-Blocks is synthesized in a commercial 22-nm FD-SOI technology and achieves a full-system energy efficiency of 115 MOPS/mW on a common FFT benchmark, 1.45× higher than a highly tuned embedded RISC-V processor. Comparable energy efficiency is obtained on multiple complex workloads, making R-Blocks a promising acceleration target for general-purpose computing.

The rapid updates in error-resilient applications along with their quest for high throughput has motivated designing fast approximate functional units for field-programmable gate arrays (FPGAs). Studies have proposed various imprecise functional techniques, albeit posed with three shortcomings: first, most existing inexact multipliers and dividers are specialized for application-specific integrated circuit (ASIC) platforms. Therefore, due to the architectural differences of underlying building blocks in FPGA and ASIC, ASIC-customized designs have not yielded comparable improvements when directly synthesized and ported to FPGAs. Second, state-of-the-art (SoA) approximate units are substituted, mostly in a single kernel of a multikernel application. Moreover, the end-to-end assessment is adopted on the quality of results (QoR), but not on the overall gained performance. Finally, the existing imprecise components are not designed to support a pipelined approach, which could boost the operating frequency/throughput of, e.g., division-included applications. In this article, we propose RAPID, the first pipelined approximate multiplier and divider architectures, customized for FPGAs. The proposed units efficiently utilize 6-input look-up tables (6-LUTs) and fast carry chains to implement Mitchell’s approximate algorithms. Our novel error-refinement scheme not only has negligible overhead over the baseline Mitchell’s approach but also boosts its accuracy to 99.4% for arbitrary size of multiplication and division. Experimental results obtained with Xilinx Vivado demonstrate the efficiency of the proposed pipelined and nonpipelined RAPID multipliers and dividers over accurate counterparts. In particular, the 4-stage pipelined architecture of a 32-bit RAPID multiplier (divider) enables$3.3\times $($5.1\times $) higher throughput,$2.3\times $($6.8\times $) higher throughput/Watt, and 52% (31%) savings of look-up tables (LUTs), over their 4-stage pipelined, accurate Intellectual Property (IP) counterparts. Moreover, the end-to-end evaluations of nonpipelined RAPID, deployed in three multikernel applications in the domains of biosignal processing, image processing, and moving object tracking for unmanned aerial vehicles (UAVs) indicate up to 35%, 33%, and 45% improvements in area, latency, and area-delay-product (ADP), respectively, over accurate kernels, with negligible loss in QoR. To springboard future research in reconfigurable and approximate computing communities, our implementations will be available and opensourced athttps://cfaed.tu-dresden.de/pd-downloads.

This paper presents a stealthy triggering mechanism that reduces the dependencies of analog hardware Trojans on the frequent toggling of the software-controlled rare nets. The trigger to activate the Trojan is generated by using a glitch generation circuit and a clock signal, which increases the selectivity and feasibility of the trigger signal. The proposed trigger is able to evade the state-of-the-art run-time detection (R2D2) and Built-In Acceleration Structure (BIAS) schemes. Furthermore, the simulation results show that the proposed trigger circuit incurs a minimal overhead in side-channel footprints in terms of area (29 transistors), delay (less than 1ps in the clock cycle), and power (1μW).

Among the SAT-resilient logic locking techniques, the Stripped-Functionality-Logic-Locking (SFLL) is the most promising solution which can guard the intellectual property against approximate, sensitization, SAT, and structural attacks which target Point-function techniques. However, even the SFLL technique has been shown to be vulnerable to a recent class of structural attacks that identify the perturbation logic. In this paper, we first categorize all possible classes of attacks on SFLL. Then we propose ENTANGLE a novel logic locking technique built upon SFLL that can resist all of these attacks, including the emerging ML-Based attacks. We test our technique against publicly available SFLL attacks. The implementation results show that ENTANGLE can secure large-sized industrial circuits with an average overhead of 11.6 percent and 9.1 percent for area and power, respectively.

Demand for coarse grain reconfigurable architectures (CGRAs) has significantly increased in recent years as architectures need to be both energy efficient and flexible. However, most CGRAs are optimized for performance instead of energy efficiency. In this work, a novel paradigm for reconfigurable architectures, Blocks, is presented. Blocks uses two separate circuit-switched networks, one for control and one for the data path. This enables the runtime construction of energy-efficient application-specific VLIW-SIMD processors on a reconfigurable fabric. Its energy efficiency is demonstrated by comparing Blocks to four reference architectures, a VLIW, an SIMD, a commercial low-power microprocessor, and a traditional CGRA. All comparisons are based on commercial low-power 40-nm CMOS layout, including memories. Results show that Blocks can achieve a mean total energy reduction of$2.05\times $,$1.84\times $,$8.01\times $, and$1.22\times $over a VLIW, an SIMD, an energy-efficient microprocessor and a traditional CGRA, respectively. At the same time, Blocks delivers equal or higher performance per area due to its ability to adapt to applications by reconfiguration.

Reconfigurable architectures are quickly gaining in popularity due to their flexibility and ability to provide high energy efficiency. However, reconfigurable systems allow for a huge design space. Iterative design space exploration (DSE) is often required to achieve good Pareto points with respect to some combination of performance, area, and/or energy. DSE tools depend on information about hardware characteristics in these aspects. These characteristics can be obtained from hardware synthesis and net-list simulation, but this is very time-consuming. Therefore, architecture models are common. This work introduces CGRA-EAM (Coarse-Grained Reconfigurable Architecture - Energy & Area Model), a model for energy and area estimation framework for coarse-grained reconfigurable architectures. The model is evaluated for the Blocks CGRA. The results demonstrate that the mean absolute percentage error is 15.5% and 2.1% for energy and area, respectively, while the model achieves a speedup of close to three orders of magnitude compared to synthesis.

Demand for coarse grain reconfigurable architectures has significantly increased as architectures need to be both energy efficient and flexible. However, most CGRAs are optimized for performance instead of energy efficiency. In this work Blocks is presented. Blocks uses two separate circuit-switched networks, one for control and one for the data-path. The unique structure of these networks enables run-time construction of energy-efficient application-specific VLIW-SIMD processors. Energy efficiency is demonstrated by comparing Blocks to a traditional CGRA on 40nm layout. Results show an energy overhead reduction between 46% and 76% and total energy reduction between 9% and 29%, depending on the benchmark. Demonstrating that the cost of flexibility can be lower than might be expected.

Due to their flexibility and high performance, Coarse Grained Reconfigurable Array (CGRA) are a topic of increasing research interest. However, CGRAs also have the potential to achieve very high energy efficiency in comparison to other reconfigurable architectures when hardware optimizations are applied. Some of these optimizations are common for more traditional processors but can also lead to large efficiency gains for reconfigurable architectures. This paper investigates three hardware based loop optimization techniques that can significantly improve the energy efficiency of CGRAs. The three techniques are evaluated on processing kernels from the image processing domain as well as an industrial computer vision application. Energy consumption and area estimates are obtained using a CGRA synthesized with a commercial 40nm library. For the three applied techniques (zero-overhead loop accelerator, single-cycle loop support, and loop buffers) the simulation results show overall energy gains of 6.8% for zero-overhead loop support, 13.2% for ZOLA combined with single-cycle loop support and 18.3% for a combination of all optimizations.

Good tool support is essential for computing platforms because they increase the programmability of the platform. This is especially the case for reconfigurable architectures because an application needs to be mapped on the architecture for each configuration individually. This paper investigates how the LLVM framework can be used to generate code for a Coarse Grained Reconfigurable Array (CGRA). A CGRA compiler must be retargetable to support all possible architecture configurations. The explicit bypassing capabilities of the hardware should be utilized. Utilizing the hardware features requires the compiler to support software pipelining, multiple register files and operation based scheduling. This paper evaluates the potential of the LLVM framework and identifies missing features for the support of reconfigurable explicit datapath architectures.

Mismatch between operand width and hardware operation width is a source of energy inefficiency. This work proposes multi-granular arithmetic, which can adapt the hardware operation width to the application, preventing energy being wasted. In particular multi-granular arithmetic in the context of coarse-grain reconfigurable architectures is considered for the operations of addition, accumulation, multiplication, and multiply-accumulation. Using a silicon synthesis-toolflow it is shown that the multi-granular designs can perform narrow width operations, e.g. an 8-by-8 multiplication, much more efficiently than standard full-width circuits. For multiplication the required energy is reduced by up to 15 times under realistic conditions when compared to a full-width 32x32 multiplier.

Approximate computing, the technique that sacrifices certain amount of accuracy in exchange for substantial performance boost or power reduction, is one of the most promising solutions to enable power control and performance scaling towards exascale. Although most existing approximation designs target the emerging data-intensive applications that are comparatively more error-tolerable, there is still high demand for the acceleration of traditional scientific applications (e.g., weather and nuclear simulation), which often comprise intensive transcendental function calls and are very sensitive to accuracy loss. To address this challenge, we focus on a very important but long ignored approximation unit on today's commercial GPUs --- the special-function unit (SFU), and clarify its unique role in performance acceleration of accuracy-sensitive applications in the context of approximate computing. To better understand its features, we conduct a thorough empirical analysis on three generations of NVIDIA GPU architectures to evaluate all the single-precision and double-precision numeric transcendental functions that can be accelerated by SFUs, in terms of their performance, accuracy and power consumption. Based on the insights from the evaluation, we propose a transparent, tractable and portable design framework for SFU-driven approximate acceleration on GPUs. Our design is software-based and requires no hardware or application modifications. Experimental results on three NVIDIA GPU platforms demonstrate that our proposed framework can provide fine-grained tuning for performance and accuracy trade-offs, thus facilitating applications to achieve the maximum performance under certain accuracy constraints.

In modern embedded systems, heterogeneous architectures are crucial in achieving desired performance requirements under area and energy constraints. Many of these systems combine a multi-processor system-on-chip and a Field Programmable Gate Array to enable hardware acceleration. Although the introduction of High-Level Synthesis significantly reduced the complexity of utilizing these systems, a programmer is still required to have expert knowledge of both the High-Level Synthesis tool and the target hardware and to perform time consuming manual iterations to achieve efficient implementations. In this paper we present SPINE, a design flow for automatic generation of efficient hardware accelerators based on Algorithmic Species. SPINE allows the designer to focus on the algorithm by automatically applying hardware specific optimizations and parallelization techniques to the design. As a case study, we present a design space exploration of nine different loop-nests used in image processing kernels and show how SPINE rapidly generates multiple area-performance trade-offs. Furthermore, we compare our results the state of the art and show that SPINE is a promising direction for accelerator generation as the average performance and area improvement with SPINE are respectively 107% and 75% over the state of the art.

Heterogeneous Multiprocessor systems-on-chip (HMPSoC) are becoming popular as a means of meeting energy efficiency requirements of modern embedded systems. However, as these HMPSoCs run multimedia applications as well, they also need to meet realtime requirements. Designing HMPSoCs with predictable timing behavior is a key challenge, as the current design methods for these platforms are semi-automated, non-predictable, or support limited heterogeneity. In this demonstration, we present a design framework to rapidly generate and implement predictable HMPSoC designs. It takes the application specifications and the architecture model as input and generates the entire HMPSoC, for FPGA prototyping, that meets the throughput constraints of the application. We also present results of a case study that computes the performance-power tradeoffs of an industrial vision application. A tool-chain targeting the Xilinx Zynq FPGA is also presented.