Search

This paper presents an overview for low-power successive approximation register (SAR) analog-to-digital converters (ADCs). It covers the operation principle, error analysis, and practical design issues. Furthermore, this paper provides a comprehensive survey of state-of-the-art low-power design techniques for every circuit block in the SAR ADC, including comparator, capacitive digital-to-analog converter (DAC), and SAR logic. The goal of this paper is to provide a useful overview to SAR ADC designers who want to improve the energy efficiency targeting low-to-medium speed applications. [ABSTRACT FROM AUTHOR]

In recent years, fast computation, low power, and small footprint are the key motivations for building SNN hardware. The unique features of SNN hardware have not been fully exploited, where the computation speed and energy efficiency of the SNN hardware can be improved according to the sparse spiking and non-uniform traffic of SNN. In this paper, we propose a 5.28-mm $^{2}~4096$ -neuron 1M-synapse energy-efficient digital SNN hardware that can achieve ultra-low energy per synaptic operation of 4.5 pJ. The proposed neuron computing unit is implemented in pipeline architecture to achieve high synaptic processing speed. The proposed spike processing unit can significantly increase the processing speed of the neuron core by $1.9\times $ and $9.4\times $ when the spike injection rate is 50% and 10%, respectively. Besides, the increase in the processing speed of the neuron core leads to a reduction in energy consumption of up to 81.5%. An event-driven clock gating circuit that can reduce the power consumption of the proposed neuron block by more than 70% is proposed in this paper. This paper proposes a supervised STDP+ algorithm for SNN training, and the classification accuracy of the MNIST digits is 89.6% with 73.6% weight sparsity of the output layer. [ABSTRACT FROM AUTHOR]

The rising popularity of intelligent mobile devices and the computational cost of deep learning-based models call for efficient and accurate on-device inference schemes. We propose a novel model compression scheme that allows inference to be carried out using bit-level sparsity, which can be efficiently implemented using in-memory computing macros. In this paper, we introduce a method called BitS-Net to leverage the benefits of bit-sparsity (where the number of zeros are more than number of ones in binary representation of weight/activation values) when applied to compute-in-memory (CIM) with resistive RAM (RRAM) to develop energy efficient DNN accelerators operating in the inference mode. We demonstrate that BitS-Net improves the energy efficiency by up to 5x for ResNet models on the ImageNet dataset. [ABSTRACT FROM AUTHOR]

In many sensor applications, a high-resolution analog-to-digital converter (ADC) is a key block. The use of an incremental delta-sigma ADC (IADC) is often well suited for such applications. While the energy-efficiency of IADCs has improved by several orders of magnitude over the past decade, the implementation of high performance IADCs, especially in battery-powered systems, is still challenging. This paper presents a tutorial review on energy-efficient IADCs and addresses the progress in this area. This paper describes the fundamentals of IADCs and energy-efficient hybrid IADC architectures. Various design techniques for improving the energy-efficiency of the IADCs are described. This paper is intended to serve as a starting point for the development of a new energy-efficient IADC. [ABSTRACT FROM AUTHOR]

This paper presents a nonlinear digital decoder (reconstruction filter) for incremental delta-sigma modulators. This decoder utilizes both the magnitude and pattern information of the modulator output to achieve accurate input estimation. Compared to the conventional linear filters with the same oversampling ratio (OSR), it can improve the converter’s signal-to-quantization noise ratio by a few dB to a few 10’s of dB with slight thermal noise performance degradation. Using the proposed decoder, the modulator’s OSR can be a few times less while achieving the same resolution and data rate, thus minimizing the modulator as well as its peripheral circuits’ energy consumption. In this paper, the proposed decoder is optimized for digital implementation, with its function being verified using a modulator prototype. This decoder is mainly designed for dc or near-dc signal conversions and it does not provide frequency notches. [ABSTRACT FROM AUTHOR]

This paper proposes a power-efficient and memory-optimized FPGA-based solution for the memory and compute-intense re-gridding process used in implementing Non-uniform Fast Fourier Transform (NuFFT) algorithm. Re-gridding refers to mapping non-equispaced sampled data onto a uniform grid using an interpolation kernel function. Re-gridding is the most time-consuming step in the entire NuFFT computation. The proposed solution is based on better utilization of FPGA resources and minimizing the number of accesses to the external memory. We demonstrate high performance over a wide range of configurations and data-sizes. This paper targets a generic solution to arbitrary sampling trajectories and gives trajectory specific solutions for some well-known trajectories in NuFFT applications, such as magnetic resonance imaging and synthetic aperture radar. Compared with existing solutions, throughput is improved by over 9.6 when compared with the existing FPGA-based techniques, whereas computational power efficiency (in terms of MFLOPS/Watt) is improved by over 15 times. Compared with GPU-based technique, 9.59 times higher MFLOPS per watts are achieved. The proposed architecture is implemented using hardware description language as well as high-level synthesis (HLS)-based OpenCL framework and the comparison is reported. Hence, this paper also serves as a reference for the comparison of HLS-based solutions against optimized HDLs. Accuracy of the re-gridding process is also reported in terms of signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

Low-power mobile devices such as RFID tags and WSNs that employ AES cryptographic modules are susceptible to differential power analysis (DPA) attacks. This paper presents a novel secured quasi-adiabatic logic (SQAL) technology that is both low-power and DPA immune. The efficiency of the SQAL technology was evaluated on an 8-bit AES-128 SBOX block and proved to be robust against DPA attacks. Compared to other adiabatic and non-adiabatic logic styles, the SQAL technology achieves better results in terms of power consumption and area overhead. [ABSTRACT FROM AUTHOR]

DRAM devices require periodic refresh operations to preserve data integrity. Slowing down the refresh rate can reduce the energy consumption; however, it may cause a loss of data stored in the DRAM cell. This paper proposes a new memory architecture of soft approximation for deep learning applications, which reduces the refresh energy consumption while maintaining accuracy and high performance. Utilizing the error-tolerant property of deep learning applications, the proposed memory architecture avoids the accuracy drop caused by data loss by flexibly controlling the refresh operation for different bits, depending on their criticality. For data storage, the approximate DRAM architecture reorganizes the data so that these data are sorted according to their bit significance. Critical bits are stored in more frequently refreshed devices while non-critical bits are stored in less frequently refreshed devices. In addition, for further reduction of the DRAM energy consumption, this paper combines hard approximation, which reduces the number of accesses to DRAM, with soft approximation. Simulation results show that the refresh energy consumption is reduced by 69.71%, and the total energy consumption is reduced by 26.0 % for the hybrid memory with a negligible drop in both training and testing phases on state-of-the-art deep networks. [ABSTRACT FROM AUTHOR]

This paper presents an energy-efficient spectral analysis method for the Internet of Things (IoT). The objective of this paper is to reduce the energy consumption of edge devices. The proposed method uses an autoregressive (AR) model for spectral analysis instead of the discrete Fourier transform, and its calculation process is distributed to the edge device and a base station by considering the energy consumption tradeoff of the data processing and the data communication. In this paper, the Yule–Walker method is employed for the AR coefficient calculation. The calculation process of Yule–Walker method can be divided into two parts: an autocorrelation calculation and an AR coefficient calculation. The autocorrelation calculation is implemented in the edge devices, and its dedicated hardware is designed using Verilog HDL. Meanwhile, the AR coefficient is calculated in the base station and is used for the spectral analysis. According to this distributed processing approach, the energy consumption of the edge device can be reduced compared with conventional DFT approaches using the fast Fourier transform (FFT). The system level energy consumption is evaluated assuming the IoT edge device, which has a wireless transceiver using Bluetooth low energy. The evaluation results show that the proposed method can reduce 79% of the edge device energy consumption for spectral analysis in a practical application. [ABSTRACT FROM AUTHOR]

This paper presents a dynamic timing control technique to prevent timing errors in a pipeline under variations. Timing errors in a pipeline are prevented by borrowing time from the following stage and resolving the borrowed time by stretching the next clock cycle. This paper analyzes the operating principles of the proposed technique; presents the design of the required circuit components; and demonstrates its operation through fabrication and measurement of a prototype test-chip designed in an 180 nm CMOS process. The measurement results demonstrate that a system employing the dynamic timing control technique can operate in a wider frequency and voltage range. [ABSTRACT FROM AUTHOR]

SRAM based computing-in-memory (SRAM-CIM) techniques have been widely studied for neural networks (NNs) to solve the “Von Neumann bottleneck”. However, as the scale of the NN model increasingly expands, the weight cannot be fully stored on-chip owing to the big device size (limited capacity) of SRAM. In this case, the NN weight data have to be frequently loaded from external memories, such as DRAM and Flash memory, which results in high energy consumption and low efficiency. In this paper, we propose a hybrid-device computing-in-memory (HD-CIM) architecture based on SRAM and MRAM (magnetic random-access memory). In our HD-CIM, the NN weight data are stored in on-chip MRAM and are loaded into SRAM-CIM core, significantly reducing energy and latency. Besides, in order to improve the data transfer efficiency between MRAM and SRAM, a high-speed pipelined MRAM readout structure is proposed to reduce the BL charging time. Our results show that the NN weight data loading energy in our design is only 0.242 pJ/bit, which is 289 $\times $ less in comparison with that from off-chip DRAM. Moreover, the energy breakdown and efficiency are analyzed based on different NN models, such as VGG19, ResNet18 and MobileNetV1. Our design can improve $\mathbf {58\times \,\,to\,\,124\times }$ energy efficiency. [ABSTRACT FROM AUTHOR]

Computing in memory (CIM) is a promising candidate for high throughput and energy-efficient data-driven applications, which mitigates the well-known memory bottleneck in Von Neumann architecture. In this paper, we present a reconfigurable bit-serial operation using toggle spin-orbit torque magnetic random access memory (TSOT-MRAM) to perform the computation completely in the bit-cell array instead of in a peripheral circuit. This bit-serial CIM (BSCIM) scheme achieves higher throughput and energy efficiency in CIM. First, basic Boolean logic operations are realized by utilizing the feature of TSOT device. A bit-cell array that implements the bit-serial operation is then built to provide the communication between column and row necessary for arithmetic operations, such as the carry propagation of addition and multiplication. Finally, we analyze the reliability of BSCIM scheme and demonstrate the performance advantage by performing convolution operations for $28\times 28$ handwritten digit images in a BSCIM architecture. The results show that the delay and energy of BSCIM architecture are respectively reduced by 1.16-5.49 times and 1.12-1.43 times compared with the existing digital CIM architectures. Besides, its throughput and energy efficiency are also enhanced to 51.2 GOPS and 9.9 TOPS/W respectively. [ABSTRACT FROM AUTHOR]

By their very nature, Spin Waves (SWs) excited at the same frequency but different amplitudes, propagate through waveguides and interfere with each other at the expense of ultra-low energy consumption. In addition, all (part) of the SW energy can be moved from one waveguide to another by means of coupling effects. In this paper we make use of these SW features and introduce a novel non Boolean algebra based paradigm, which enables domain conversion free ultra-low energy consumption SW based computing. Subsequently, we leverage this computing paradigm by designing a non-binary spin wave adder, which we validate by means of micro-magnetic simulation. To get more inside on the proposed adder potential we assume a 2-bit adder implementation as discussion vehicle, evaluate its area, delay, and energy consumption, and compare it with conventional SW and 7 nm CMOS counterparts. The results indicate that our proposal diminishes the energy consumption by a factor of $3.14 \times $ and $6 \times $ , when compared with the conventional SW and 7 nm CMOS functionally equivalent designs, respectively. Furthermore, the proposed non-binary adder implementation requires the least number of devices, which indicates its potential for small chip real-estate realizations. [ABSTRACT FROM AUTHOR]

When deploying deep neural networks (DNNs), edge devices training is practical to improve model adaptivity for various user-specific scenarios while avoiding privacy disclosure. However, the training computation is intolerable for edge devices. It inspires sparse DNN training (SDT) into the limelight, which reduces training computation by dynamic weight pruning. Generally, SDT has two strategies based on the pruning granularity: the structured or the unstructured. Unfortunately, both of them suffer from limited training efficiency due to the gap between pruning granularity and hardware implementation. The former is hardware-friendly but has a low pruning ratio, indicating limited computation reduction. The latter has a high pruning ratio, but the unbalanced workload decreases utilization and irregular sparsity distribution causes considerable sparsity processing overhead. This paper proposes a software-hardware co- design to bridge the gap for improving the efficiency of SDT. On the algorithm side, a sub-structured pruning method, achieved with hybrid shape-wise and line-wise pruning, generates a high sparsity ratio and keeps the hardware-friendly property. On the hardware side, a sub-structured weight processing unit (SWPU) effectively handles the hybrid sparsity with three techniques. First, SWPU dynamically reorders the computation sequence with hamming-distance-based clustering, balancing the irregular workload. Second, SWPU performs runtime scheduling by exploiting the feature of sub-structured sparse convolution through a detect-before-load controller, which skips redundant memory access and sparsity processing. Third, SWPU performs sparse convolution by compressing operands with spatial disconnect log-based routing and recovers their location with bi-directional switching, avoiding the power-consumed routing logic. Synthesized with 28nm CMOS technology, SWPU can enable 0.56V-to-1.0V supply voltage with a maximum frequency of 675 MHz. It achieves a 50.1% higher pruning ratio than structured pruning and $1.53\times $ higher energy efficiency than unstructured pruning. The peak energy efficiency of SWPU is 126.04TFLOPS/W, outperforming the state-of-the-art training processor by $1.67\times $. When training a ResNet-18 model, SWPU reduces $3.72\times $ energy and offers $4.69\times $ speedup than previous sparse training processors. [ABSTRACT FROM AUTHOR]

Edge device deep neural network (DNN) training is practical to improve model adaptivity for unfamiliar datasets while avoiding privacy disclosure and huge communication cost. Nevertheless, apart from feed-forward (FF) as inference, DNN training still requires back-propagation (BP) and weight gradient (WG), introducing power-consuming floating-point computing requirements, hardware underutilization, and energy bottleneck from excessive memory access. This paper proposes a DNN training processor named PL-NPU to solve the above challenges with three innovations. First, a posit-based logarithm-domain processing element (PE) adapts to various training data requirements with a low bit-width format and reduces energy by transferring complicated arithmetics into simple logarithm domain operation. Second, a reconfigurable inter-intra-channel-reuse dataflow dynamically adjusts the PE mapping with a regrouping omega network to improve the operands reuse for higher hardware utilization. Third, a pointed-stake-shaped codec unit adaptively compresses small values to variable-length data format while compressing large values to fixed-length 8b posit format, reducing the memory access for breaking the training energy bottleneck. Simulated with 28nm CMOS technology, the proposed PL-NPU achieves a maximum frequency of 1040MHz with 343mW and 5.28mm $\mathbf {^{2}}$. The peak energy efficiency is 3.87TFLOPS/W for 0.6V at 60MHz. Compared with the state-of-the-art training processor, PL-NPU reaches $3.75\times $ higher energy efficiency and offers $1.68\times $ speedup when training ResNet18. [ABSTRACT FROM AUTHOR]

Next generation neural recording and brain-machine interface (BMI) devices call for high density or distributed systems with more than 1000 recording sites. As the recording site density grows, the device generates data on the scale of several hundred megabits per second (Mbps). Transmitting such large amounts of data induces significant power consumption and heat dissipation for the implanted electronics. Facing these constraints, efficient on-chip compression techniques become essential to the reduction of implanted systems power consumption. This paper analyzes the communication channel constraints for high density neural recording devices. This paper then quantifies the improvement on communication channel using efficient on-chip compression methods. Finally, This paper describes a Compressed Sensing (CS) based system that can reduce the data rate by $> 10 \times$ times while using power on the order of a few hundred nW per recording channel. [ABSTRACT FROM AUTHOR]

Beyond 5G systems are expected to approach 1 Tb/s throughput. This poses a significant challenge to the channel decoder. In this paper, we propose a multi-core architecture based on full row parallel layered LDPC decoder with frame interleaving. Compared with conventional partially parallel layered architectures, the proposed architecture increases the throughput by applying frame interleaving into the pipeline architecture and by using multi-core architectures. Two high rate medium size QC LDPC codes are designed with fast decoding convergence speed for this architecture. Both codes are implemented with single core and multi-core architectures to explore different trade-offs between code design, communication performance and implementation. The four decoders are implemented in 16 nm CMOS FinFET technology with a clock rate of 1 GHz. The placement and routing implementation results show that the single core decoder for the LDPC (1027, 856) code is able to provide 114 Gb/s throughput at maximum 3 iterations with an area of 0.173 mm2 and energy efficiency of 1.56 pJ/bit; the multi-core decoder for the (1032, 860) code is able to provide 860 Gb/s throughput at maximum 2 iterations with an area of 1.48 mm2 and energy efficiency of 3.24 pJ/bit. The multi-core decoder achieves the highest throughput in the literature for medium size (1–2k) LDPC codes. When compared with other state-of-the-art fully parallel high speed architectures, the proposed architectures bring a significant gain both in area efficiency and energy efficiency while keeping the ability to offer flexibility in code rate, number of iterations and early stop. [ABSTRACT FROM AUTHOR]

This paper presents an advanced offline scheduling scheme to improve the accelerator efficiency, especially for the highly-pruned convolutional neural networks (HP-CNNs). Based on the existing outlier-aware accelerator design, we demonstrate the efficiency drop of HP-CNN processing for the first time, and element-wise channel merging is proposed to make a dense processing sequence even for the highly-pruned model. The dedicated hardware architecture is also presented to process the merged channels with the minimum hardware-level overheads, improving the energy efficiency for handling HP-CNNs by preserving the hardware utilization. We further investigate the optimal size of accumulator and multiplexer, in addition to the number of merged channels, exploiting the attractive energy-performance trade-offs. As a result, unlike the practical HP-CNNs for the on-device solutions, the proposed method enhances the overall efficiency by up to 33% compared to the state-of-the-art schemes. [ABSTRACT FROM AUTHOR]

One key factor affecting the distributed Nash equilibrium (NE) seeking in aggregative games is the unbalanced communication structure for multiple players. Although some results on seeking NE over undirected or weight-balanced graphs were established, how to address the distributed NE seeking problem over general directed communication graphs is still an outstanding challenge. This paper addresses the NE seeking problem for a class of aggregative games with general directed communication graphs. To achieve this objective, two new kinds of distributed discrete-time NE seeking algorithms are developed for aggregative games over fixed digraphs and time-varying digraphs, respectively. In particular, motivated by the heavy-ball method in optimization studies, a momentum term is introduced to the update law of the players’ actions and it is numerically verified that this momentum term accelerates the convergence of the proposed algorithms. For both strongly connected fixed graph and $B$ -strongly connected time-varying graph, it is theoretically proved that the actions of players will converge to the NE of aggregative games for the case of decreasing step-size implemented by the proposed NE seeking algorithms if the cost functions and the aggregation of players satisfy some certain conditions. Finally, the developed NE seeking algorithms are applied to the energy consumption control of plug-in hybrid electric vehicles (PHEVs), which demonstrates the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

Tree-based machine learning models, like random forests and decision trees, are low-complexity solutions that provide an efficient prediction for a wide range of applications. These models are particularly interesting for energy-constrained platforms since they can be implemented with simple logical operations. Tree-based accelerators are also intrinsically resilient to errors, and this can be leveraged to boost energy efficiency with approximate computing techniques. The key operations in these models are comparisons to constants, making comparators excellent candidates for approximation. This paper presents a technique to approximate comparisons to constants called C2PAx, which is capable of reducing the area and energy of tree-based accelerators. The method consists in finding alternative constants that reduce circuit area while keeping an efficient prediction performance. It is also shown that the selection of the constant parameters directly influences both hardware complexity and model performance, demanding cross-layer optimization. For that, we extend an existing framework that generates VLSI tree-based accelerators, inserting our approximation proposal that allows selecting the constant parameters that maximize energy efficiency at the cost of minor accuracy drops. Simulation results demonstrate that C2PAx outperforms the Don’t Care logic approximation technique when accuracy and energy are jointly considered. C2PAx trades accuracy for significant reductions in the VLSI area, power, delay, and energy consumption compared to precise models. [ABSTRACT FROM AUTHOR]

This paper presents a low-power and area efficient 10-bit successive approximation register (SAR) analog-to-digital (ADC) with a hybrid capacitive-MOS consisting of a 7-bit MSB capacitive DAC (CDAC) and a 3-bit LSB MOS DAC (MDAC), which consumes less power and much smaller chip area than a pure CDAC. Instead of using a string of eight MOS transistors to control one unit capacitor, the 3-bit LSB MDAC is realized by a MOS string with four native MOS transistors to control two unit capacitors, which allows higher voltage drop and more reliable operation for each unit MOS. The overall energy consumption of the proposed CAP-MOS DAC is reduced by 56.2% compared to a Vcm-based 10-bit pure CDAC with the same unit capacitance. Under the sampling rate of 200 kS/s, the prototype 10-bit SAR ADC implemented in a 0.18- ${\mu }\text{m}$ CMOS technology achieves a signal-to-noise-and-distortion ratio / spurious-free dynamic range of 56.91 /68.56 dB at 99-kHz input under a 0.6-V power-supply, while consumes $1.76~{\mu }\text{W}$ at 200 kS/s for a figure of merit of 15.38 fJ/step. The peak DNL and INL are +0.27/−0.21 LSB and +0.43/−0.45 LSB, respectively. The ADC occupies a small active area of 0.097 mm2. [ABSTRACT FROM AUTHOR]

This paper identifies novel directions of standard-cell-based synthesizable memory design. A compact 18T-bitcell of OR-AND-Invert (OAI) and AND-OR-Invert (AOI) logic gates is presented with bit-selective write and multiplexed read accesses. It reduces the bitcell area by 11%–40% compared to the state-of-the-art clock-gating-based D-latch schemes while avoiding custom-cell design. The improved storage density comes from the tight integration on two levels–coupling the read multiplexing within each bitcell and taking advantage of structured datapath placement in bitcell arrays by using commercial CAD tools. As measured on a 40 nm SRAM of 90 kb, the OAI/AOI-gate-based synthesizable memory features 0.4 V minimum access voltage, 30 fJ minimum read energy per bit, and a leakage power of 1.4 pW per bit at 0.3 V data retention voltage. It is ideally suited for IoT-node design operating in sub- $V_{\mathrm {t}}$ for joint dynamic energy and leakage power reduction. Further, it enables fast turn-around times by IP reuse over technology nodes with minimal (re)design effort. [ABSTRACT FROM AUTHOR]

Ten methods for finding through simulations the small-signal phase and gain margins of feedback circuits based on op-amps are described and analyzed in this mostly tutorial paper. The testbenches employed by these methods are presented and the corresponding analytical expressions of the return ratio are derived and compared against their “ideal” counterpart, obtained with standard circuit analysis; the requirement that the return ratio should not depend on the point it was measured at is also verified. These analyses are performed on a fairly general case: a generic reciprocal two-port network that closes a feedback loop around an op-amp acting as the forward amplifier. The four main types of op-amps were considered. The limitations of some of the tested methods are then highlighted by simulations. Besides the detailed analysis of previously reported methods, the paper proposes a novel method for deriving the return ratio of feedback circuits, that employs only current stimuli; it is demonstrated analytically that this method can be used for bilateral circuits, not only for op-amp-based (unilateral) ones. Also, a recent method for deriving directly the phase margin of a circuit is extended to estimating the gain margin, too. Conclusions on the accuracy and suitability of the analyzed methods for practical circuit cases are drawn. These results are then extended to other circuit topologies. [ABSTRACT FROM AUTHOR]

Due to its high resolution, small silicon area, and reasonable conversion rate, extended counting analog to digital conversion (EC-ADC) has become a competitive candidate for application in the readout circuits of high accuracy sensors. In this paper, an EC-ADC for large scale sensor arrays is proposed. At the system level, a double-sampling configuration for incremental \Sigma\Delta modulators is proposed. By applying this configuration, the clock frequency of EC-ADC can be almost halved for the same conversion accuracy and rate. At the circuit level, the contribution of class-AB operational transconductance amplifier (OTA) structure to the low power design is analyzed and a class-AB OTA applicable for EC-ADC is developed. The proposed architecture has been implemented using a commercial 0.18 \mum CMOS technology. Measured results show that a power consumption of less than 77 \muW can be achieved at the conversion rate of 51.2 Ksamples/s and 12.2-bit accuracy respectively. The converter core occupies 0.044 mm^2. [ABSTRACT FROM AUTHOR]

Incremental analog-to-digital-converters (IADCs) are variants of $\triangle \Sigma $ ADCs, which have been increasingly used for low-power sensory applications in recent years. Most IADC applications require high resolution and high energy efficiency. In this paper, we present a systematic analysis of IADCs. We derive analytical design equations for practical IADC designs. Process limitations are included in the model as well. The equations are verified with a 14-bit second-order IADC design in a 0.18- $\mu \text{m}$ process. With the design equations, the theoretical energy efficiency bound is derived for IADCs. The efficiency bound is compared with previously reported IADC designs. It is found that the derived bound matches existing designs well. [ABSTRACT FROM AUTHOR]

Single constant multiplication (SCM) and multiple constant multiplications (MCM) are among the most popular schemes used for low-complexity shift-add implementation of finite impulse response (FIR) filters. While SCM is used in the direct form realization of FIR filters, MCM is used in the transposed direct form structures. Very often, the hybrid form FIR filters where the sub-sections are implemented by fixed-size MCM blocks provide better area, time, and power efficiency than those of traditional MCM and SCM based implementations. To have an efficient hybrid form filter, in this paper, we have performed a detailed complexity analysis in terms of the hardware and time consumed by the hybrid form structures. We find that the existing hybrid form structures lead to an undesirable increase of complexity in the structural-adder block. Therefore, to have a more efficient implementation, a variable size partitioning approach is proposed in this paper. It is shown that the proposed approach consumes less area and provides nearly 11% reduction of critical path delay, 40% reduction of power consumption, 15% reduction of area-delay product, 52% reduction of energy-delay product, and 42% reduction of power-area product, on an average, over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The twelve papers in this special section were presented at the 2013 IEEE Custom Integrated Circuits Conference (CICC 2013)that was held in San Jose, CA, September 2013. [ABSTRACT FROM AUTHOR]

A design methodology for making magnetic coupling wireless power transfer (MC-WPT) systems resonate at given frequencies is proposed in this paper. Proposing such a method not only can improve the energy efficiency against frequency splitting, but also can satisfy the requirement of frequency standards and then promote its industrial application. The method is based on eigenvalues configuration and comprehensively considers all electric parameters. The quantitative relationship between the performance indexes and electric parameters is derived, based on which the expected electric parameters can be quickly and accurately determined. The design theory and design flow are provided, and practical systems are designed, simulated and measured to show the effectiveness of the proposed method. The results show that the proposed method can: 1) eliminate frequency splitting to make MC-WPT systems resonate at a given frequency; 2) utilize frequency splitting to make MC-WPT systems resonate at two given frequencies. [ABSTRACT FROM AUTHOR]

This paper reports a half-rate bang-bang clock and data recovery (BBCDR) circuit supporting the trimodal (NRZ/PAM-4/PAM-8) operation. The observation of their crossover- points distribution at the transitions introduces the single-loop phase tracking technique. In addition, low-power techniques at both the architecture and circuit levels are employed to greatly improve the overall energy efficiency and multiply data throughput by increasing the number of levels on the magnitude. Fabricated in 28-nm CMOS, our BBCDR prototype scores a 0.29/0.17/0.14 pJ/bit efficiency at 14.4/28.8/43.2 Gb/s under NRZ/PAM-4/PAM-8 modes, respectively. The jitter is < 0.53 ps (integrated from 100 Hz to 1 GHz) with approximately-equivalent constant loop bandwidth, and we achieve at least 1-UIpp jitter tolerance up to 10 MHz for all the three modes. [ABSTRACT FROM AUTHOR]

Spiking neural networks (SNNs), which are inspired by the human brain, have recently gained popularity due to their relatively simple and low-power hardware for transmitting binary spikes and highly sparse activation maps. However, because SNNs contain extra time dimension information, the SNN accelerator will require more buffers and take longer to infer, especially for the more difficult high-resolution object detection task. As a result, this paper proposes a sparse compressed spiking neural network accelerator that takes advantage of the high sparsity of activation maps and weights by utilizing the proposed gated one-to-all product for low power and highly parallel model execution. The experimental result of the neural network shows 71.5% mAP with mixed (1,3) time steps on the IVS 3cls dataset. The accelerator with the TSMC 28nm CMOS process can achieve $1024\times 576.29$ frames per second processing when running at 500MHz with 35.88TOPS/W energy efficiency and 1.05mJ energy consumption per frame. [ABSTRACT FROM AUTHOR]

For the design of ring oscillator-based ADCs, little has been reported on how to optimally co-design the readout scheme and the ring oscillator core towards optimal energy efficiency. This paper describes a methodology to find this optimum for a target SQNR and signal bandwidth, for the case of 1st-order quantization noise shaping. In short, starting from initial assumptions on the VCO, the readout optimization boils down to finding the best VCO frequency. From this, the number of readout phases, the sampling frequency of the readout and the number of bits in the quantizers are derived. Then, it is explained how to adjust the VCO core toward the corresponding optimal VCO configuration. This is discussed for the case of a current-controlled ring oscillator, indicating the main constraints that define the design space for such a VCO optimization. If the actual VCO configuration is bounded by practical constraints, this approach can be re-iterated where a new readout optimization is performed taking these constraints into account. [ABSTRACT FROM AUTHOR]

Quantization is a promising technique to compress the size of Convolutional Neural Network (CNN) models. Recently, various precision-scalable designs have been presented to reduce the computational complexity in CNNs. However, most of them adopt straightforward calculation scheme to implement the CNN, which causes high bandwidth requirement and low hardware utilization efficiency. This paper proposes a new precision-scalable architecture which can fully reduce the computational complexity in CNN inference and meanwhile has a finely simplified calculation scheme. Based on the proposed scheme, a well-optimized multiplier called Compositional Processing Element (C-PE) is devised. Compared with the previous multipliers, the new C-PE requires less area and power. Furthermore, two levels of optimization are introduced to the design to relieve the bandwidth problem and increase the hardware utilization efficiency. Implemented under the TSMC 90nm CMOS technology, the whole design achieves 6-68.1 fps in various precisions on VGG16 benchmark and a 49.8TOPS/W energy efficiency at 500MHz when scaled to 28nm, which is much better than previous precision-scalable ones. [ABSTRACT FROM AUTHOR]

Deep Neural Networks (DNNs) have recently shown state-of-the-art results on various applications, such as computer vision and recognition tasks. DNN inference engines can be implemented in hardware with high energy efficiency as the computation can be realized using a low-precision fixed point or even binary precision with sufficient cognition accuracies. On the other hand, training DNNs using the well-known back-propagation algorithm requires high-precision floating-point computations on a CPU and/or GPU causing significant power dissipation (more than hundreds of kW) and long training time (several days or more). In this paper, we demonstrate a training chip fabricated using a commercial 65-nm CMOS technology for machine learning. The chip performs training without back propagation by using invertible logic with stochastic computing that can directly obtain weight values using input/output training data with low precision suitable for inference. When training neurons that compute the weighted sum of all inputs and then apply a non-linear activation function, our chip demonstrates a reduction of power dissipation and latency by 99.98% and 99.95%, respectively, in comparison with a state-of-the-art software implementation. [ABSTRACT FROM AUTHOR]

The output ripple of switched-capacitor dc–dc voltage converter (SCVC) severely degrades the energy efficiency, performance, and robustness of the VLSI system. In this paper, a fully digital resistance modulation (FDRM) technique is proposed to reduce the output ripple of SCVC, which is scalable and compatible with the exiting ripple reduction methods. The proposed FDRM technique suppresses the impulsive charging and discharging effects in the SCVC operation by dynamically modulating its equivalent switch resistances, resulting in a reduced output ripple. The FDRM control signals can be generated from simple logic gates with the interleaved clock signals, realizing a low implementation complexity. The proposed FDRM technique was verified by a fully integrated SCVC in 180-nm CMOS process with an active area of 0.93 mm2. The measurement results show that the SCVC prototype with the proposed FDRM technique achieves an averaged ripple reduction of 31.6% and a peak conversion efficiency of 88.96% under a loading range of 95–190 $\mu \text{A}$. [ABSTRACT FROM AUTHOR]

As convolutional neural networks (CNNs) become more and more diverse and complicated, acceleration of CNNs increasingly encounters a bottleneck of balancing performance, energy efficiency, and flexibility in a unified architecture. This paper proposed a Winograd-based highly efficient and dynamically Reconfigurable Accelerator (named WRA) for quickly evolving CNN models. A cost-effective convolution decomposition method (CDW) was proposed, and it extends the application of the fast Winograd algorithm. Based on CDW, a high-throughput and reconfigurable processing element (PE) array was designed to exploit the parallelism of Winograd. Besides, a highly compact memory structure employed four levels of data reuse schemes to achieve maximal data reuse and minimize external bandwidth requirement. Provided with dynamically reconfigurable capability, WRA implements CDW and other convolutions (e.g., standard convolution, depthwise separable convolution, and group convolution) on a unified hardware architecture. The WRA accelerator was implemented on a Xilinx XCVU9P platform running at 330 MHz clock frequency, controlled by a POWER8 processor via a coherent accelerator processor interface (CAPI) interface. At different configurations, WRA can provide 2.2–6.3 TOPS performance for different convolution shapes. The average performance and energy efficiency for VGG16/AlexNet/MobileNetV1/MobileNetV2 are 5288 GOP/s at 151.2 GOPs/W, 3478 GOP/s at 99.4 GOPs/W, 2674 GOP/s at 76.4 GOPs/W, and 2194 GOP/s at 62.7 GOPs/W. It achieves $1.7\times $ – $24\times $ speedup compared with the previous FPGA-based designs. [ABSTRACT FROM AUTHOR]

This paper introduces a transregional energy model to estimate the energy consumption of digital designs for voltages widely ranging from nominal down to deep sub-threshold. The model also predicts the optimal voltage at which the minimum energy point (MEP) occurs, and the resulting minimum energy achievable under aggressive voltage scaling. The model provides the energy profile versus voltage based on a few technology-dependent parameters that are extracted through simple circuit simulations, and other few design-dependent parameters extracted from synthesis or Place&Route reports at nominal voltage only. The model captures the effect of input-dependent (e.g., switching activity), technology-dependent (e.g., corner, transistor flavor), and micro-architectural parameters (e.g., logic depth). The model exhibits a typical error below 10%, and is well suited for design exploration under wide voltage scaling, as it avoids detailed and time-consuming multi-corner and multi-voltage iterations. Also, the model provides a quick estimate of the achievable MEP for a given design. Accordingly, the proposed model is a useful tool that supports early selection of the most energy-efficient design options to meet a targeted energy profile at low voltages. [ABSTRACT FROM AUTHOR]

In recent years, the study of lightweight symmetric ciphers has gained interest due to the increasing demand for security services in constrained computing environments, such as in the Internet of Things. However, when there are several algorithms to choose from and different implementation criteria and conditions, it becomes hard to select the most adequate security primitive for a specific application. This paper discusses the hardware implementations of Present, a standardized lightweight cipher called to overcome part of the security issues in extremely constrained environments. The most representative realizations of this cipher are reviewed and two novel designs are presented. Using the same implementation conditions, the two new proposals and three state-of-the-art designs are evaluated and compared, using area, performance, energy, and efficiency as metrics. From this wide experimental evaluation, to the best of our knowledge, new records are obtained in terms of implementation size and energy consumption. In particular, our designs result to be adequate in regards to energy-per-bit and throughput-per-slice. [ABSTRACT FROM PUBLISHER]

Computing-in-memory (CIM) is a new architecture which is more energy-efficient than the Von Neumann architecture due to the fact that it performs calculation in the memory units which can reduce a large amount of data movement. Nowadays, CIM with non-volatile memory (nvCIM), such as resistive random access memory (RRAM), has become a research frontier to further improve computing performance. Recent works mainly explored how to improve the computing performance of nvCIM, but seldom paid attention to the problem of accuracy loss. In this paper, we propose the nvCIM framework, which can systematically analyze the relationship between the classification accuracy of network models and main analog factors. Based on the nvCIM framework, we further provide detailed optimization methods, including RRAM features, array properties, and ADC parameters. The adaptive voltage-controlled SET and pulse-controlled RESET (VSPR) program-verify scheme is proposed to achieve high-resolution RRAM. And the margin enhancement based current-mode sense amplifier (MECSA) and offset reduction based analog-to-digital converter (ORADC) are proposed to improve the accuracy of analog part computing. Experimental results show that the macro-level and system-level energy efficiency is 112.1 TOPS/W and 9.86 TOPS/W respectively with less than 3.51% loss in contrast to the ideal accuracy, which is $2.9\times $ - $25.9\times $ compared to the energy efficiency of existing RRAM based nvCIM accelerators. [ABSTRACT FROM AUTHOR]

This paper presents a buffer-embedded noise-shaping SAR ADC, whose input buffer separates the capacitive DAC (CDAC) and the sampling capacitor (CS) at the input and output of the input buffer. This compensates for the non-linearity of the input buffer and reduces the CS value, resulting in a significant power saving in the input buffer. This buffer-embedded architecture enables the effective implementation of the following passive loop filter and enhances energy efficiency. A bootstrapping switch in the feedback CDAC is coupled to the output of the buffer, thereby avoiding a signal dependency due to the parasitic capacitance of the switch. The buffer-embedded noise-shaping SAR ADC occupies 0.08mm2 in a 65 nm CMOS process and features a parasitic input capacitor of 0.2 pF. It achieves 73.8 dB SNDR, 77 dB DR and 87.3 dB SFDR in a 2 MHz bandwidth without any calibration. Including the power consumption of the input buffer, the ADC consumes only 2.1 mW. [ABSTRACT FROM AUTHOR]

Neuromorphic computing can solve computationally hard problems with energy efficiencies unattainable for von Neumann architectures. A locally-active memristor, which possesses the capability to amplify infinitesimal fluctuations in energy and can be used to generate neuromorphic behaviors, is a natural candidate for constructing an electronic equivalent of biological neurons. This paper identifies some unknown neuromorphic dynamics of the Chua corsage memristor (CCM), and shows that the CCM, when biased at the edge of chaos domain, can exhibit rich dynamics of biological neurons. Using Chua’s theories of local activity and edge of chaos, we demonstrate that under the destabilizing of the input voltage and the circuit parameters (inductance or capacitance), two CCM-based circuits can produce thirteen types of neuromorphic behaviors either on, or near the edge of chaos domain via supercritical or subcritical Hopf bifurcation. In addition, we give the conditions to test the edge of chaos of the CCM and the CCM-based circuit only by using the poles and the zero of their admittance functions. [ABSTRACT FROM AUTHOR]

This paper discusses finite/fixed-time (FNT/FXT) synchronization of multi-layer networks based on energy consumption estimation. Firstly, a novel FXT stability theorem of dynamical system is presented. Secondly, sufficient criteria for FNT/FXT synchronization in multi-layer networks are given, and the estimation of the energy consumption are also gained. Especially, the range of the number of layers of network is gained based on the least convergence time and the estimation of the least energy consumption. The results show that shorter synchronization time and less energy consumption does not mean less network layers. Finally, the validity of the theoretical method is verified by numerical simulation. [ABSTRACT FROM AUTHOR]

This paper presents a novel ultra-low voltage level shifter for fast and energy-efficient wide-range voltage conversion from sub-threshold to I/O voltage. By addressing the voltage drop and non-optimal feedback control in a state-of-the-art level shifter based on Wilson current mirror, the proposed level shifter with revised Wilson current mirror significantly improves the delay and power consumption while achieving a wide voltage conversion range. It also employs mixed-Vt device and device sizing aware of inverse narrow width effect to further improve the delay and power consumption. Measurement results at 0.18 \mum show that compared with the Wilson current mirror based level shifter, the proposed level shifter improves the delay, switching energy and leakage power by up to 3\times, 19\times, 29\times respectively, when converting 0.3 V to a voltage between 0.6 V and 3.3 V. More specifically, it achieves 1.03 (or 1.15) FO4 delay, 39 (or 954) fJ/transition and 160 (or 970) pW leakage power, when converting 0.3 V to 1.8 V (or 3.3 V), which is better than several state-of-the-art level shifters for similar range voltage conversion. The measurement results also show that the proposed level shifter has good delay scalability with supply voltage scaling and low sensitivity to process and temperature variations. [ABSTRACT FROM AUTHOR]

This paper presents a 44.2-mW 3.2-GHz 3-port register file (RF) that demonstrates measured operation from 1.2 V down to 0.4 V. The 32-entry \times \,32 -bit/word 2-read/1-write RF is fabricated in TSMC 65-nm low-power low threshold voltage (low- \text {V}_{\mathrm {t}} ) CMOS process. A four-transistor read port is presented that permits the design of low-capacitance dynamic local bitlines (LBLs). Switching power in the LBLs and the LBL precharge buffer is thereby reduced. Based on extensive simulation results, the proposed read port is recommended for use in wide-worded RFs, which employ a wide dynamic-OR structure at the LBL stage. The proposed RF outperforms the conventional design in terms of power consumption for frequencies exceeding 3-GHz. The read port exploits intrinsic capacitive coupling to achieve robust operation over a wide voltage range. The architecture of the read port simultaneously enhances robustness of the dynamic bitline by 58.8% as compared to the conventional low- \text {V}_{\mathrm {t}}$ bitline. [ABSTRACT FROM PUBLISHER]

This paper presents a 14-bit 250 MS/s ADC fabricated in a 180 nm CMOS process, which aims at optimizing its linearity, operating speed, and power efficiency. The implemented ADC employs an improved SHA with parasitic optimized bootstrapped switches to achieve high sampling linearity over a wide input frequency range. It also explores a dedicated foreground calibration to correct the capacitor mismatches and the gain error of residue amplifier, where a novel configuration scheme with little cost for analog front-end is developed. Moreover, a partial non-overlapping clock scheme associated with a high-speed reference buffer and fast comparators is proposed to maximize the residue settling time. The implemented ADC is measured under different input frequencies with a sampling rate of 250 MS/s and it consumes 300 mW from a 1.8 V supply. For 30 MHz input, the measured SFDR and SNDR of the ADC is 94.7 dB and 68.5 dB, which can remain over 84.3 dB and 65.4 dB for up to 400 MHz. The measured DNL and INL after calibration are optimized to 0.15 LSB and 1.00 LSB, respectively, while the Walden FOM at Nyquist frequency is 0.57 pJ/step. [ABSTRACT FROM AUTHOR]

In this paper, a method for offline training of inverter-based memristive neural networks (IM-NNs), called ERIM, is presented. In this method, the output voltage of the inverter is modeled very accurately by considering the loading effect of the memristive crossbar. To properly choose the size of each inverter, its output load and the required slope of its voltage transfer characteristic (VTC) for an acceptable level of resiliency to the circuit element non-idealities are taken into account. The efficacy of ERIM is investigated by comparing its accuracy to those of two recently proposed offline training methods for IM-NNs (RIM and PHAX). The study is performed using IRIS, BCW, MNIST, and Fashion MNIST datasets. Simulation results show that 72% (56%) reduction in average energy consumption of the trained networks is achieved compared to RIM (PHAX) thanks to proper sizing of the inverters. In addition, due to the higher accuracy of the NN mathematical model, ERIM results in significant improvements in the match between the results of high-level modeling and HSPICE simulations while exhibiting lower sensitivity to circuit element variations. [ABSTRACT FROM AUTHOR]

In this paper, a two-stage reconfiguration strategy is proposed for a group of agents to find their specially designated formation, which can be seen as a transition from the current states to the predefined formations. The specially designated formation occurs during the inter-task idle time in order to minimize the reconfiguration time. The basic specially designated formation is designed for dotted agents based on the optimal mapping relationships and current probabilities of the predefined formations. Then, the reconfiguration schemes are developed with the agents modeled as circles or ellipses to account for their physical shapes, and the problem of agent overlapping during the formation is addressed. Examples and simulation results are presented to analyze and demonstrate the effectiveness and potential of the new design techniques. [ABSTRACT FROM AUTHOR]

This paper presents a charge-domain in-memory computing (IMC) macro for precision-scalable deep neural network accelerators. The proposed Dual-SRAM cell structure with coupling capacitors enables charge-domain multiply and accumulate (MAC) operation with variable-precision signed weights. Unlike prior charge-domain IMC macros that only support binary neural networks or digitally compute weighted sums for MAC operation with multi-bit weights, the proposed macro implements analog weighted sums for energy-efficient bit-scalable MAC operations with a novel series-coupled merging scheme. A test chip with a 16-kb SRAM macro is fabricated in 28-nm FDSOI process, and the measured macro throughput is 125.2-876.5 GOPS for weight bit-precision varying from 2 to 8. The macro also achieves energy efficiency ranging from 18.4 TOPS/W for 8–b weight to 119.2 TOPS/W for 2-b weight. [ABSTRACT FROM AUTHOR]

This paper presents an integrated ultra-low-power (ULP) wireless sensor system-on-chip (SoC) that can be used for voltage sensing in both Internet of Things applications and bio-potential monitoring. In order to increase the energy efficiency of the analog front-end (AFE), we propose a noise and power efficient push-pull low noise instrumentation amplifier (LNIA) with a built-in ripple reduction loop based on capacitor reuse. A low-power ISM-band harmonic injection locked on-off-keying transmitter (OOK-TX) is also implemented for energy efficient wireless connectivity. Circuit implementations, design considerations, and detailed analysis are presented to improve the overall energy efficiencies of the SoC including the AFE, TX and, the power management unit. The proposed ULP-SoC is fabricated in 130 nm CMOS technology with a total area of 1.92 mm2. The total power consumption of the proposed system-on-chip is 197.1 μW which is one of the lowest among state-of-the-art wireless sensor SoC. [ABSTRACT FROM AUTHOR]

In emerging Spiking Neural Network (SNN) based neuromorphic hardware design, energy efficiency and on-line learning are attractive advantages mainly contributed by bio-inspired local learning with nonlinear dynamics and at the cost of associated hardware complexity. This paper presents a novel SNN design employing fast COordinate Rotation DIgital Computer (CORDIC) algorithm to achieve fast spike timing–dependent plasticity (STDP) learning with high hardware efficiency. In this study, a system design and evaluation method of CORDIC-based SNN is proposed for finding optimal CORDIC type and precision, from theoretical CORDIC-level error to application-level learning performance. From the proposed design and evaluation method, a reconfigurable SNN design based on fast-convergence CORDIC is designed to achieve high classification accuracy on MNIST, fast on-line learning and good energy efficiency. By utilizing SNN’s fault tolerance and time-division-multiplexing (TDM) strategy, the reconfigurable SNN design employs 8-bit fast-convergence CORDIC and TDM-based hardware accelerator for high efficiency. FPGA implementation results confirm that the proposed fast-convergence CORDIC SNN design outperforms the state-of-the-art CORDIC method by 38.5%−45.3% in terms of learning speed and energy efficiency, with the STDP learning of 30.2 ns/SOP, energy efficiency of 176.6 pJ/SOP, processing speed of 6.1 ms/image, and on-line learning convergence of 21.4 s (time to reach the final accuracy, on average), on MNIST benchmark. [ABSTRACT FROM AUTHOR]

Video transrating has become an essential task to allow the transmission of different versions of the same video in streaming services and live applications. However, as the transrating operation comprises a decoding and an encoding step in sequence, it demands high processing time and energy consumption, which is prohibitive in large-scale systems. This paper proposes a scalable quality and time/energy-aware high-efficiency video coding (HEVC) transrating system based on decision trees. The scalable scheme operates under three different modes that employ the decision tree outcomes in different ways according to the desired tradeoff between image quality and time/energy savings. Experimental results presented a transrating time reduction of up to 57.5%, with a minimum energy consumption reduction of 49.5% and an average memory bandwidth reduction of 24% in comparison to the original transcoder. These results were achieved at the cost of a Bjøntegaard delta (BD) rate increase of only 0.664% in the most conservative transrating mode, which allows a transrating time reduction of 48.5%. The proposed decision trees were implemented as an IP core and synthesized targeting 45-nm ASIC technology, achieving the capability of processing $7680\times 4320$ videos at 240 frames/s with a negligible power consumption of 0.849 mW. [ABSTRACT FROM AUTHOR]