Temporal Convolutional Networks (TCNs) involving mono channels as input, have shown superior performance compared to state-of-the-art sequence detection recursive networks in a variety of applications. TCNs leverage the concept of dilated causal convolution for a wider receptive field coverage of input (mono) channels, which requires scaling the delay between input samples in Multiply-Accumulate (MAC) units in different layers. We demonstrate a possible data-flow transformation to convert a dilated convolution to a non-dilated convolution to remove such need for delay scaling while maintaining the same receptive field. The new data-flow transformation allows for hardware units to be shared across all layers with single-delay units between the MAC units. We demonstrate how such data-flow transformation can be easily achieved using generic Finite Impulse Response (FIR) filter modules, simplifying the deployment of TCNs. We validate the predicted savings using Cadence Stratus High-Level Synthesis (HLS). A gesture recognition case study using ultrasound is synthesized achieving 25% savings in both energy and area if the data-flow transformation is applied.

Abstract Cell-aware test (CAT) explicitly targets faults caused by defects inside library cells to improve test quality, compared with conventional automatic test pattern generation (ATPG) approaches, which target faults only at the boundaries of library cells. The CAT methodology consists of two stages. Stage 1, based on dedicated analog simulation, library characterization per cell identifies which cell-level test pattern detects which cell-internal defect; this detection information is encoded in a defect detection matrix (DDM). In Stage 2, with the DDMs as inputs, cell-aware ATPG generates chip-level test patterns per circuit design that is build up of interconnected instances of library cells. This paper focuses on Stage 1, library characterization, as both test quality and cost are determined by the set of cell-internal defects identified and simulated in the CAT tool flow. With the aim to achieve the best test quality, we first propose an approach to identify a comprehensive set, referred to as full set, of potential open- and short-defect locations based on cell layout. However, the full set of defects can be large even for a single cell, making the time cost of the defect simulation in Stage 1 unaffordable. Subsequently, to reduce the simulation time, we collapse the full set to a compact set of defects which serves as input of the defect simulation. The full set is stored for the diagnosis and failure analysis. With inspecting the simulation results, we propose a method to verify the test quality based on the compact set of defects and, if necessary, to compensate the test quality to the same level as that based on the full set of defects. For 351 combinational library cells in Cadence’s GPDK045 45nm library, we simulate only 5.4% defects from the full set to achieve the same test quality based on the full set of defects. In total, the simulation time, via linear extrapolation per cell, would be reduced by 96.4% compared with the time based on the full set of defects.

Integrated circuits operating in the near/subthreshold region offer low energy consumption. However, due to the constrained voltage scalability of SRAMs, efficient power delivery is difficult to achieve. A traditional implementation would require at least two distinct voltage supplies generated by possibly two power converters. In this article, a new implementation for near/subthreshold operation is presented. The proposed implementation consists of a new “converter-free” design based on a three-level voltage stack operating at 1.8 V ± 5%. Here, the leakage current from the SRAMs in the top stack is recycled to sustain the near/subthreshold operation of the logic circuits in the two lower stacks. A test chip with the proposed voltage-stacking technique was implemented in a 28-nm low- Vth (LVT) fully depleted silicon on insulator (FDSOI) technology. The test chip is an ultralow-power advanced system-on-chip (SoC) consisting of an RISC-V core, a coarse-grained reconfigurable accelerator, and peripherals. The SoC uses a current sink and an adaptive body-bias controller for voltage regulation of the intermediate voltage rails between the stacks. The proposed system achieves up to 95% power delivery efficiency with negligible area overhead (~ 1%). The silicon measurement shows that the system energy efficiency is improved by 1.6× on average, and the energy consumption is reduced by 37% on average compared to the flat implementation.

In this work, a design of an IEEE 802.15.4 con-formant O-QPSK demodulator is proposed, which is capable of trading off receiver sensitivity for power savings. Such design can be used to meet rigid energy and power constraints for many applications in the Internet-of-Things (IoT) context. In a Body Area Network (BAN), for example, the circuits need to operate with extremely limited energy sources, while still meeting the network performance requirements. This challenge can be addressed by the paradigm of adequate computing, which trades off excessive quality of service for power or energy using approximation techniques. Three different, adjustable approximation techniques are integrated into the demodulation to trade off effective signal quantization bit-width, filtering performance, and sampling frequency for power. Such approximations impact incoming signal sensitivity of the demodulator. For detailed trade-off analysis, the proposed design is implemented in a commercial 40-nm CMOS technology to estimate power and in Python to estimate sensitivity. Simulation results show up to 64% power savings by sacrificing $\tilde 7$ dB sensitivity.

On the trend of ultrasound-based gesture recognition, this study introduces the concept of time-sequence classification of ultrasonic patterns induced by hand movements on a microphone array. We refer to time-sequence ultrasound echoes as continuous frequency patterns being received in real-time at different steering angles. The ultrasound source is a single tone continuously being emitted from the center of the microphone array. In the interim, the array beamforms and locates an ultrasonic activity (induced echoes) after which a processing pipeline is initiated to extract band-limited frequency features. These beamformed features are organized in a 2D matrix of size 11 × 30 updated every 10ms on which a Temporal Convolutional Network (TCN) outputs continuous classification. Prior to that, the same TCN is trained to classify Doppler shift variability rate. Using this approach, we show that a user can easily achieve 49 gestures at different steering angles by means of sequence detection. To make it simple to users, we define two Doppler shift variability rates; very slow and very fast which the TCN detects 95-99% of the time. Not only a gesture can be performed at different directions but also the length of each performed gesture can be measured. This leverages the diversity of inair ultrasonic gestures allowing more control capabilities. The process is designed under low-resource settings; that is, given the fact that this real-time process is always-on, the power and memory resources should be optimized. The proposed solution needs 6.2 - 10.2 MMACs and a memory footprint of 6KB allowing such gesture recognition system to be hosted by energy- constrained edge devices such as smart-speakers.

Cell-aware test (CAT) explicitly targets faults caused by cell-internal short and open defects and has been shown to significantly reduce test escape rates. CAT library cell characterization is typically done for only two defect resistance values: one representing hard opens and another one representing hard shorts. In this paper, similar to fishermen tightening the mesh size of their nets to catch small fish, we perform library characterization as efficiently as possible for a set of resistances representing increasingly weaker defects, and then adjust our ATPG flow to explicitly target faults caused by the weakest still-detectable variant of each potential defect. We implemented this novel approach in an experimental ATPG tool flow script, using functions of Cadence's Modus as building blocks. To assess the effectiveness of our approach, we formulate a new dedicated test metric: the weakest fault coverage wfc. Compared to conventional CAT targeting hard defects only, experimental results show that our new approach enhances detection of weakest faults and significantly reduces wfc escapes =1-wfc, while maintaining its original (hard-defect) fault coverage fc, of course at the expense of (acceptable) increases in the required number of test patterns and associated test generation time.

This paper presents the design and evaluation of an energy-efficient seizure detection system for emerging EEG-based monitoring applications, such as non-convulsive epileptic seizure detection and Freezing-of-Gait (FoG) detection. As part of the BrainWave system, a BrainWave processor for flexible and energy-efficient signal processing is designed. The key system design parameters, including algorithmic optimizations, feature offloading and near-threshold computing are evaluated in this work. The BrainWave processor is evaluated while executing a complex EEG-based epileptic seizure detection algorithm. In a 28-nm FDSOI technology, 325 μJ per classification at 0.9 V and 290 μJ at 0.5 V are achieved using an optimized software-only implementation. By leveraging a Coarse-Grained Reconfigurable Array (CGRA), 160 μJ and 135 μJ are obtained, respectively, while maintaining a high level of flexibility. Near-threshold computing combined with CGRA acceleration leads to an energy reduction of up to 59%, or 55% including idle-time overhead.

With the increasing demand on voice recognition services, more attention is paid to simpler algorithms that are capable to run locally on a hardware device. This paper demonstrates simpler speech features derived in the time-domain for Keyword Spotting (KWS). The features are considered as constrained lag autocorrelations computed on overlapped speech frames to form a 2D map. We refer to this as Multi-Frame Shifted Time Similarity (MFSTS). MFSTS performance is compared against the widely known Mel-Frequency Cepstral Coefficients (MFCC) that are computed in the frequency-domain. A Temporal Convolutional Network (TCN) is designed to classify keywords using both MFCC and MFSTS. This is done by employing an open source dataset from Google Brain, containing ~ 106000 files of one-second recorded words such as, 'Backward', 'Forward', 'Stop' etc. Initial findings show that MFSTS can be used for KWS tasks without visiting the frequency-domain. Our experimental results show that classification of the whole dataset (25 classes) based on MFCC and MFSTS are in a very good agreement. We compare the performance of the TCNbased classifier with other related work in the literature. The classification is performed using small memory footprint (~ 90 KB) and low compute power (~ 5 MOPs) per inference. The achieved classification accuracies are 93.4% using MFCC and 91.2% using MFSTS. Furthermore, a case study is provided for a single-keyword spotting task. The case study demonstrates how MFSTS can be used as a simple preprocessing scheme with small classifiers while achieving as high as 98% accuracy. The compute simplicity of MFSTS makes it attractive for low power KWS applications paving the way for resource-aware solutions.

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.

Arithmetic circuits like adders and multipliers are key workforces of many error resilient applications. Prior efforts on approximating these arithmetic circuits mainly focused on manual circuit level functional modifications. These manual approaches need high design time and effort. Due to this only a limited no. of approximate design points can be generated from the original circuit leading to a sparsely occupied pareto front. This work proposes an automated approximation methodology for arithmetic circuits. Proposed method approximates the gate level standard cell library and uses these approximate standard cells to modify the netlist of the original circuit. A heuristic design space exploration methodology is proposed to speed-up the design process. We integrate this methodology with traditional ASIC flow and validate our results using adders and multipliers of different bitwidths. We show that our methodology improves on existing state-of-the-art manual as well as automated design techniques by generating non-dominant pareto-fronts. An application case study (sobel edge detection) is shown using approximate arithmetic circuits generated by our methodology. In case of sobel edge detector, we show upto 50% energy improvements for hardly any quality degradation (PSNR ≥ 20dB).

Cell-aware test (CAT) explicitly targets defects inside library cells and therefore significantly reduces the number of test escapes compared to conventional automatic test pattern generation (ATPG) approaches that cover cell-internal defects only serendipitously. CAT consists of two steps, viz. (1) library characterization and (2) cell-aware ATPG. Defect detection matrices (DDMs) are used as the interface between both CAT steps; they record which cell-internal defects are detected by which cell-level test patterns. This paper proposes two algorithms that manipulate DDMs to optimize cell-aware ATPG results with respect to fault coverage, test pattern count, and compute time. Algorithm 1 identifies don't-care bits in cell patterns, such that the ATPG tool can exploit these during cell-to-chip expansion to increase fault coverage and reduce test-pattern count. Algorithm 2 selects, at cell level, a subset of preferential patterns that jointly provides maximal fault coverage at a minimized stimulus care-bit sum. To keep the ATPG compute time under control, we run cell-aware ATPG with the preferential patterns first, and a second ATPG run with the remaining patterns only if necessary. Selecting the preferential patterns maps onto a well-known NP-hard problem, for which we derive an innovative heuristic that outperforms solutions in the literature. Experimental results on twelve circuits show average reductions of 43% of non-covered faults and 10% in chip-pattern count.

Advanced technology nodes employ a large number of innovations. In addition, they require `scaling boosters' in the design of standard-cell libraries to be able to offer the scaling benefits in area, performance, and power that we have grown accustomed to. Consequently, sub-10nm standard cells are significantly more complex than their predecessors. Cell-aware test (CAT) explicitly targets cell-internal resistive open and short defects identified through extensive characterization of the library cells. This paper is (to the best of our knowledge) the first to report on the application of CAT library characterization on a sub-10nm technology node. We used Cadence's CAT tool flow on an experimental 114-cell-library in IMEC's 3nm CMOS technology iN5. Despite the increased cell complexity, we show that the CAT flow still works, and that compared with functionally-comparable library cells in a 45nm technology, the number of potential non-equivalent defect locations, cell-level test patterns, and defect coverage did not change drastically.

Many error resilient applications can be approximated using multi-layer perceptrons (MLPs) with insignificant degradation in output quality. Faster and energy efficient execution of such an application is achieved using a neural accelerator (NA). This work exploits the error resilience characteristics of a MLP by approximating the accelerator itself. An error resilience analysis of the MLP is performed to obtain key constraints which are used for designing energy efficient approximate multipliers. A systematic methodology for the design of approximate multipliers is used. A graph based netlist modification approach is considered. Approximate versions of basic standard cells are generated and these are used to replace accurate cells in the synthesized netlist in a systematic quality controlled manner. These approximate multipliers are further used for approximating the multiply and accumulate (MAC) units in the neural accelerator (NA). The results are validated by considering approximate neural replication of a robotic application, inversek2j. System level energy savings of upto 14% is obtained for less the 7% degradation in output quality. Average application speedup of 24% is obtained over accurate neural accelerator (NA). The results are compared with state-of-the-art approximate multipliers and a comparison with truncation (bit-wise scaling) is performed. Moreover, error healing capability of MLPs is shown by studying the impact of retraining on networks with approximate multipliers.

Pulsed-latches emerge as an ideal sequencing element for low power digital circuit design, serving as an alternative of flip-flops. In this paper, low power multi-bit pulsed-latches are proposed to construct pipeline stages in synchronous digital circuits. A method of integrating the proposed multi-bit pulsed-latches in the commercial design flows is also introduced. With the multi-bit pulsed-latches, up to 45% power savings are achieved for a variety of ITC benchmark circuits and an ARM Cortex-M0 as compared to the flip-flop based designs in an industrial 28-nm FDSOI CMOS technology. Furthermore, the power consumption of the clock distribution network and the layout area are reduced by up to 83% and 16%, respectively, with the proposed multi-bit pulsed-latches as compared to the flip-flop based designs.

Process variability severely impacts the performance of circuits operating in the subthreshold domain. Among other reasons, this mainly stems from the fact that subthreshold current follows a widely spread Log-Normal distribution. In this paper we introduce a new transistor sizing methodology for standard cells. Our premise relies on balancing the N and P network currents based on statistical formulations. Our approach renders more robust cells. We observe up to 57% better performance and 69% lower energy consumption on a set of ISCAS circuits when they are synthesized with our library as opposed to a commercial library in a CMOS 90nm technology.

This paper presents litho friendly circuit techniques for variability resilient low power 8T SRAM. The new local assist circuitry achieves a state-of-the-art low energy and variability resilient WRITE operation and improves the degraded access speed of SRAM cells at low voltages. Differential VSS bias increases the variability resilience. The physical regularity in the layout of local assist circuitry enables litho optimization thereby reducing the area overhead associated with existing local assist techniques. Statistical simulations in 40nm LP CMOS technology reveals 10x reduction in WRITE energy consumption, 103x reduction in write failures, 6.5x improvement in read access time and 31% reduction in the area overhead.

Sub-threshold operation has been proved to be successful to achieve minimum energy consumption. It is well known that the sub-threshold device sizing is different from super-threshold due to different current behavior. The previously reported sub-threshold sizing methods assume that the current is proportional to the transistor width. However, we have found that the inverse narrow width effect has a significant influence on the threshold voltage in the sub-threshold region, causing non-proportional current-width relationship. Sizing without considering this effect may result in significant imbalance in the rise and fall delay which degrades the performance, power consumption and the functional yield of the design. We have proposed a new sub-threshold sizing method to balance the rise and fall delay by taking into account the influence of inverse narrow width effect while minimizing the transistor size. Compared with the previous sub-threshold sizing method the delay and power-delay-product (PDP) are reduced by up to 35.4% and 73.4% with up to 57% saving in the area. Further, due to symmetric rise and fall delay the minimum operating voltage can be lowered by 8% which leads to another 16% of energy reduction.

This paper presents a system design study for wearable sensor devices intended for healthcare and lifestyle applications based on ECG, EEG and activity monitoring. In order to meet the low-power requirement of these applications, a dual-core signal processing system is proposed which combines an ultra-low-power bio-medical Application Specific Instruction-set Processor (BioASIP) and a low-power general-purpose micro-controller (MSP430). To validate the merits of the proposed architecture, system-level power analysis and trade-offs are conducted using real hardware measurements of an ECG R-peak detection application. The results show that the proposed dual-core architecture consumes around 65.38µW, about 25.8x smaller than an MSP430-only approach. Out of 65.38µW, the BioASIP consumes only 11µW and the rest is used in the analog front-end, A/D conversion, and control tasks.

Due to the unattended nature of WSN (Wireless Sensor Network) deployment, each sensor can be subject to physical capture, cloning and unauthorized device alteration. In this paper, we use the embedded SRAM, often available on a wireless sensor node, for secure data (cryptographic keys, IDs) generation which is more resistant to physical attacks. We evaluate the physical phenomenon that the initial state of a 6T-SRAM cell is highly dependent on the process variations, which enables us to use the standard SRAM circuit, as a Physical Unclonable Function (PUF). Important requirements to serve as a PUF are that the start-up values of an SRAM circuit are uniquely determined, unpredictable and similar each time the circuit is turned on. We present the evaluation results of the internal SRAM memories of low power ICs as PUFs and the statistical analysis of the results. The experimental results prove that the low power 90nm commercial 6T-SRAMs are very useful as a PUF. As far as we know, this is the first work that provides an extensive evaluation of 6T-SRAM-based PUF, at different environmental, electrical, and ageing conditions to representing the typical operating conditions of a WSN.

Instruction memory organization is pointed out as one of the major sources of energy consumption in embedded systems. As embedded systems are characterized by restrictive resources and low energy budget, any enhancement in this component allows not only to decrease the total energy consumption, but also to have a better distribution of the energy budget throughout the system. This paper presents a self-tuning banked loop buffer architecture, which is based on a run-time loop buffer controller that optimizes both the dynamic and leakage energy consumption of the instruction memory organization. Results show that using banking in loop buffer architectures leads to higher reduction in the total energy consumption of the instruction memory organization if the tuning approach is applied sparingly. Based on post-layout simulations, our approach improves the total energy consumption by average of 20% in comparison with a loop buffer architecture based on a single monolithic memory, and more than 90% in comparison with instruction memory organizations without loop buffer architectures.

In order to provide better services to elderly people, home healthcare monitoring systems have been increasingly deployed. Typically, these systems are based on wireless sensor nodes, and should utilize very low energy during their lifetimes, as they are powered by scavengers. In this article, we present an ultra-low power processing system for a wireless sensor node for very low duty cycle applications. In the CoolBio system-on-chip, we utilized several power reduction techniques at both the architecture level and the circuit level. These techniques include feature extraction, voltage and frequency scaling, clock and power gating and a redesign of key standard cells. In the design of the ultra-low power processing system, we paid special attention to the memory subsystem, as it is one of the most power-consuming modules in a design. We also designed a clock manager in order to reduce the power consumed by clocking, and a power manager that is able to power-off unutilized modules. The proposed wireless sensor node processing system consumes 36.4μW at 100MHz and 1.2V supply voltage, for a heartbeat-detection algorithm with a 0.01% duty cycle.

Power consumption in digital systems, especially in portable devices, is a crucial design factor. Due to downscaling of technology, dynamic switching power is not the only relevant source of power consumption anymore as power dissipation caused by leakage currents increases. Even though power gating is a seemingly simple method for reducing the leakage power, the implications of introducing power gating to a design have to be analyzed in detail. We present an extensive analysis of the impact of fine-grained power gating on the overall power consumption. The presented results are based on the analysis of an actual implementation of power gating in the datapath of a very long instruction word (VLIW) processor. The extracted power consumption values clearly demonstrate that the overhead of power gating is, in contrary to the analysis found in previous publication, not determined by the energy required to switch a power domain on. Rather, it is determined by the energy consumption of additionally required modules. We show that, for the break-even point case, about 2/3 of the energy overhead is caused by the isolation cells, about 1/3 by the control modules, and only roughly 1% by the energy to switch a power domain on.

Near-Threshold Circuits achieve ultra-low energy operating with significant performance improvement and noise immunity as compared to sub-threshold circuits. However, near-threshold circuit performance is highly sensitive to static and dynamic threshold voltage variations. This makes designing circuits for a target performance very difficult, and post-silicon tunability is required to achieve performance targets without taking huge design margins. In this work, we tackle this problem by proposing a novel dual-Vdd technique for near-threshold operation and show that one can tune the performance of a circuit in a fine-grained manner by powering an optimal sub-set of rows with a slightly higher supply voltage than the rest, without incurring the large cost of distributed level shifters. By varying the percentage of rows at a slightly higher voltage, one can trade-off performance and power in a fine-grained manner. Experimental results show that by employing our dual-Vdd technique, we can improve the performance of several benchmarks up-to 45% while achieving more than 50% lower power as compared to single-Vdd implementations.

A growing number of applications, often with firm or soft real-time requirements, are integrated on the same System on Chip, in the form of either hardware or software intellectual property. The applications are started and stopped at run time, creating different use-cases. Resources, such as interconnects and memories, are shared between different applications, both within and between use-cases, to reduce silicon cost and power consumption. The functional and temporal behaviour of the applications is verified by simulation and formal methods. Traditionally, designers resort to monolithic verification of the system as whole, since the applications interfere in shared resources, and thus affect each other's behaviour. Due to interference between applications, the integration and verification complexity grows exponentially in the number of applications, and the task to verify correct behaviour of concurrent applications is on the system designer rather than the application designers. In this work, we propose a Composable and Predictable Multi-Processor System on Chip (CoMPSoC) platform template. This scalable hardware and software template removes all interference between applications through resource reservations. We demonstrate how this enables a divide-and-conquer design strategy, where all applications, potentially using different programming models and communication paradigms, are developed and verified independently of one another. Performance is analyzed per application, using state-of-the-art dataflow techniques or simulation, depending on the requirements of the application. These results still apply when the applications are integrated onto the platform, thus separating system-level design and application design.

Multi-processor systems on chip (MPSoC) platforms are becoming increasingly more heterogeneous and are shifting towards a more communication-centric methodology. Networks on chip (NoC) have emerged as the design paradigm for scalable on-chip communication architectures. As the system complexity grows, the problem emerges as how to design and instantiate such a NoC-based MPSoC platform in a systematic and automated way. This paper presents an integrated flow to automatically generate a highly configurable NoC-based MPSoC for FPGA instantiation. The system specification is done on a high level of abstraction, relieving the designer of error-prone and time consuming work. The flow uses the state-of-the-art /Ethereal NoC, and silicon hive processing cores, both configurable at design- and run-time. The authors use this flow to generate a range of sample designs whose functionality has been verified on a Celoxica RC300E development board. The board, equipped with a Xilinx Virtex II 6000, also offers a huge number of peripherals, and shows how the insertion is automated in the design for easy debugging and prototyping

This paper presents a high performance FFT ASIP. The resulting programmable solution is scalable for the order of the FFT and capable of satisfying performance requirements of various OFDM wireless standards. The IEEE 802.15.3a ultra wideband OFDM - being the most time critical of these standards because of the computation of a 128-point FFT within 312.5 ns - has been the primary performance target of the scalable ASIP. The resulting ASIP adopts a vectorial ultra-long instruction word (ULIW) approach. The design decisions are evaluated with regards to processing speed, area and power dissipation.

Growing complexity of multiprocessor systems on chip (MP-SoC) requires future communication resources that can only be met by highly scalable architectures. Networks-on-Chip (NoCs) offer this scalability and other advantages like modularity, quality-of-service (QoS), possibly smaller area footprint and lower power dissipation. Although many papers describe the advantages of NoCs and describe techniques to apply NoCs on certain application domains, few have actually employed the complete design chain to make a netlist level implementation and area comparison (Steenhof et al., 2006) and (Angiolini et al., 2006). This paper describes the application of the AEligthereal NoC to an existing bus-based MP-SoC design and an area comparison with the original interconnects structure down to netlist level

Low density parity check (LDPC) codes offer excellent error correcting performance. However, current implementations are not capable of achieving the performance required by next generation storage and telecom applications. Extrapolation of many of those designs is not possible because of routing congestions. This article proposes a new architecture, based on a redefinition of a lesser-known LDPC decoding algorithm. As random LDPC codes are the most powerful, we abstain from making simplifying assumptions about the LDPC code which could ease the routing problem. We avoid the routing congestion problem by going for multiple independent sequential decoding machines, each decoding separate received codewords. In this serial approach the required amount of memory must be multiplied by the large number of machines. Our key contribution is a check node centric reformulation of the algorithm which gives huge memory reduction and which thus makes the serial approach possible.

The demands in terms of processing performance, communication bandwidth and real-time throughput of new generation mobile communication applications (mobile and base-stations) are much higher than today's programmable processing architectures can deliver. On the other hand standards and market uncertainties, nonrecurring engineering costs, and lack of access to (or knowledge of) application IP will require the next generation of embedded computing platforms to be fully programmable. In terms of silicon cost and power, practical yet fully programmable embedded computing platforms are enabled by reconfigurable processors that replace fixed ASICs in current standard platforms. This paper explains the concepts behind a novel reconfigurable WCDMA rake receiver and gives benchmark results. The proposed RAKE receiver enables a high performance, yet flexible computing platform for WCDMA.

Turbo decoding offers outstanding error correcting capabilities, that will be used in wireless applications like the Universal Mobile Telecom Standard (UMTS). However the algorithm is very computational intensive, and therefore an implementation on a general purpose programmable DSP results in a power consumption which reduces the applicability of turbo decoding in hand-held applications. In this paper we present a solution based on a layered processing architecture. This architecture includes an application specific Very Long Instruction Word (VLIW) processor, a data flow processor, and hard-wired execution units in a hierarchical way. The power consumption of this solution is an order of magnitude better than the implementation on a current state of the art, power efficient general purpose DSP.

Connectivity with different infrastructures implies the use of many media, far more than the amount required to communicate with man. When we restrict ourselves to hand-held devices, this number reduces due to the need for wireless communication. The majority of hand-held devices only have a single channel to provide connectivity with the network. The number of hand-held terminals has grown dramatically, with devices like pagers, mobile phones, DECT phones, palmtop and hand-held PCs, portable CD players and television. All this should be integrated into a single device, a personal companion, which provides all the functions carried out by the above-mentioned devices. To integrate these functions in a single device is a major challenge, most prominently from the power dissipation and functional composition point of view. In the digital era, re-use and resource sharing are now possible. A strong move towards programmable solutions exists, but there is a downside to this approach: performance requirements lead to the addition of extra processors or co-processing hardware and extra power dissipation. Additionally, providing too much flexibility leads to excessive system verification time. Therefore, I don't believe in a software-biased approach, which leads to silicon solutions with CPUs and DSPs on a single chip. Heterogeneous multiprocessor architectures, covering the range from dedicated hardware to CPUs, is the playing field to keep functional composition, power and verification effort under control. Specification is the key to success for chips in multimedia hand-held terminals.

Article FADIC: architectural synthesis applied in IC design Share on Authors: J. Huisken Philips Research Laboratories, Eindhoven, The Netherlands Philips Research Laboratories, Eindhoven, The NetherlandsView Profile , F. Welten Philips Research Laboratories, Eindhoven, The Netherlands Philips Research Laboratories, Eindhoven, The NetherlandsView Profile Authors Info & Claims DAC '96: Proceedings of the 33rd annual Design Automation ConferenceJune 1996 Pages 579–584https://doi.org/10.1145/240518.240628Online:01 June 1996Publication History 0citation212DownloadsMetricsTotal Citations0Total Downloads212Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

The state of the art of compiling digital signal processing (DSP) algorithms into silicon is discussed. It is indicated how digital signal processing differs from numerical data processing, including the consequences for the synthesis tools. On the basis of a broad range of DSP applications, four classes of architectures are then distinguished to serve as templates for four different synthesis systems. Although each of these four silicon compilers is tuned to a specific class of applications in order to generate area-efficient chips, they all accept as input the same behavioral DSP specification. The four selected architectural styles are best characterized by hard-wired bit-serial data-paths, microcoded multiprocessors, cooperating bit-parallel data-paths, and regular arrays. The characteristics of the first three architectures are treated in more detail in a discussion of three different Cathedral synthesis environments for their respective design. A fourth Cathedral environment, aiming at the synthesis of regular arrays, is still in an early stage of development and is not discussed. The claims for the compilers are substantiated by typical designs.>

A 2µm CMOS Digital Signal Processor (PCB5010 / PCB5011), capable of eight million instructions per second (8MIPS), and up to 6 concurrent operations in each instruction will be described [1]. This high throughput results from a highly parallel architecture (see Fig. 1) with high-speed data handling capability. It contains two 16b data buses, two primary execution units, five I/O interfaces, a data ROM, two data RAMs, and flexible addressing of on and off-chip memory using three address computation units. Benchmarks show a two to six times improvement in overall performance over its predecessors.