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Although spin orbit torque magnetic random access memory (SOT-MRAM) is one of the strong candidates for next-generation embedded memories, the degradation of read margin due to low tunnel magnetoresistance ratio (TMR) with process variations has been a large concern. In this paper, we present the dual-domain dynamic reference (DDDR) sensing scheme, where the reference voltage can be dynamically changed based on the combined voltage and time domain sensing to increase the sensing margin. The Half Schmitt trigger and sample & hold circuits are efficiently employed to generate data-dependent reference voltages and to store the sampled voltage levels at different times, respectively. According to the simulations using 28nm CMOS technology with 128 by 128 SOT-MRAM array, the proposed DDDR approach achieves a 243mV of sensing margin under 6.08E-8 bit-error-rate (BER) at 1.76ns, which is 2X larger margin with more than 100 times lower BER compared to the conventional read scheme. When scaling down the pre-charge voltage, the proposed scheme achieves more than 50% of the read energy savings under 1E-5 target BER condition. [ABSTRACT FROM AUTHOR]

This paper deals with nonlinear control of a single-phase half-bridge interleaved buck shunt active power filter (HBIB-SAPF) with a nonlinear load. The control objective for the system is twofold: performing power factor correction by compensating for harmonics and reactive current consumed by the nonlinear load from one hand and tightly regulating the HBIB converter DC capacitor voltage. Both objectives are accomplished using a two-loop nonlinear controller. The inner loop acts on the switching devices so that the active filter current tracks its reference with the aim of ensuring a unity power factor. This loop is tackled using backstepping technique and Lyapunov approach. The outer loop is responsible for regulating the DC capacitor voltage to its desired value, using a PI controller with a pre-filter. The stability analysis of the closed-loop system is formally performed by using the averaging theory. The validity of the designed nonlinear controller is checked by simulations in Matlab/SimpowerSystem showing its robustness and accuracy under various operating conditions. [ABSTRACT FROM AUTHOR]

This paper presents a gate driver for a GaN-based half-bridge structure operating in a buck converter with input voltage >40 V or a boost converter with output voltage >30 V. Two 500 pF on-chip capacitors are utilized to construct three-level gate drivers, providing a near- $V_{{\text {DD}}}$ negative voltage for gate of the rectifier switch to eliminate the induced pulse on the gate from the high dv / dt slew rate of $V_{\text {X}}$ when the main switch is turned on. The dead time controller tunes the delay of the gate signal of the rectifier switch by sensing the slope of $V_{\text {X}}$ , thus the near-optimal zero-voltage switching can be achieved with deviation < 3 ns. The GaN driver is implemented with a 0.18- $\mu \text{m}$ BCD process. The efficiencies can be improved by 8.33% and 6.87% at light load in a buck and a boost converter due to the dead-time control. The peak efficiencies of 20 V–12 V and 12 V–18 V conversions are 86.37% and 84.39%, respectively. [ABSTRACT FROM AUTHOR]

Neuromorphic processors have gained momentum recently due to their high energy efficiency in artificial intelligence applications compared to DNN accelerators. Most neuromorphic processors are executing SNNs (Spiking Neural Networks). Liquid State Machine (LSM), as the spiking version of reservoir computing, shows advantages and great potential in image classification, speech recognition, language translation, etc.. Comparing with other SNN models, LSM has the characteristics of easy to train and low resource utilization, which is suitable for low-power and resource-constrained edge computing scenarios. In this paper, we propose a novel design of a neuromorphic processor, LSMCore, aiming at LSM acceleration. LSMCore supports both training and inference of LSM. It consists of 256 input neurons, 1024 liquid neurons, and 1.31M synapses. Besides, multiple optimization techniques, including weight quantization for reducing storage, zero-skipping for decreasing dynamic sparsity, and mini-batch training are adopted in this processor. The experimental results show that the frequency of LSMCore achieves 400 MHz, the power is 4.9W and the area is 18.49 mm2 with a 40nm library. Comparing with the baseline, LSMCore achieves up to $80.7\times $ ($49.6\times $), $91.3\times $ ($56.3\times $), and $83.1\times $ ($56.8\times $) speedup on MNIST, N-MNIST, and Free Spoken Digital Dataset (FSDD) respectively for training (inference), while the accuracy of LSMCore on these three datasets are 96.8%, 97.6%, and 90% respectively. [ABSTRACT FROM AUTHOR]

Back-bias control is a new degree of freedom brought by fully-depleted silicon-on-insulator (FDSOI) CMOS technologies, which can be used to control the oscillation frequency of voltage-controlled ring oscillators (VCROs). The resulting VCRO architecture is called a back-bias-controlled oscillator (BBCO). This paper compares it with the conventional current-starved ring oscillator (CSRO) topology in terms of power consumption and phase noise figure-of-merit (FoM), while taking practical design constraints of process-voltage-temperature (PVT) robustness and frequency tuning range into account. The proposed comprehensive analysis takes advantage of relevant and compact analytical models, as well as extensive pre-layout simulation results. The comparison is made at four different target oscillation frequencies, which are representative of frequency synthesis for WiFi/Bluetooth/LPWAN wireless communications and of clock generation for smartphone/Internet-of-Things processors: 300 MHz, 868 MHz, 2.45 GHz, and 5.18 GHz. In 28-nm FDSOI technology, the results demonstrate that BBCOs can intrinsically reach 1.69 to $4.63\times $ lower minimum power consumption and slightly better FoM values than CSROs. [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]

This paper presents an extensive study of linear and logistic regression algorithms implemented with 1T1R memristor crossbars arrays. Using a sophisticated simulation platform that wraps circuit-level simulations of 1T1R crossbars and physics-based models of RRAM (memristors), we elucidate the impact of device variability on algorithm accuracy, convergence rate and precision. Moreover, a smart pulsing strategy is proposed for practical implementation of synaptic weight updates that can accelerate training in real crossbar architectures. Stochastic multi-variable linear regression shows robustness to memristor variability in terms of prediction accuracy but reveals impact on convergence rate and precision. Similarly, the stochastic logistic regression crossbar implementation reveals immunity to memristor variability as determined by negligible effects on image classification accuracy but indicates an impact on training performance manifested as reduced convergence rate and degraded precision. [ABSTRACT FROM AUTHOR]

In this paper, self-matching rectifiers for enhanced input dynamic range are demonstrated. The method is based on an artificial transmission line, which consists of a stepped-impedance transmission line loaded with shunt varactors. The output DC voltage of the rectifier is fed back to the varactors to control the effective electrical length and characteristic impedance of the line. Because this bias voltage changes as the power input to the rectifier changes, a matching network that maintains impedance matching regardless of the input power can be developed without additional circuits, thus substantially improving the input dynamic range. The experimental results for prototype rectifiers show a dynamic range as wide as 25 dB with only a single Schottky diode, an improvement of 11 dB over the rectifier with a conventional matching scheme, in which the conversion efficiency remains above 50%. The effectiveness of the proposed method is also verified with the experimental results for rectennas based on the demonstrated rectifiers. [ABSTRACT FROM AUTHOR]

This paper proposes to utilize intentional time delays as part of controllers to improve the damping of electromechanical oscillations of power systems. Through stability theory, the control parameter settings for which these delays in Power System Stabilizers (PSSs) improve the small signal stability of a power system are systematically identified, including the key parameter settings for which stability regions in the parameter plane remain connected for effective operation. The paper shows that PSSs with two control channels can be effectively designed to achieve best damping characteristics for a wide range of delays. Analytical results are presented on the One-Machine Infinite-Bus (OMIB) electromechanical power system model. To demonstrate the opportunities in more realistic dynamic models, our results are then implemented via numerical analysis on the IEEE standard 14-bus system. [ABSTRACT FROM AUTHOR]

When applied to artificial intelligence edge devices, the conventionally von Neumann computing architecture imposes numerous challenges (e.g., improving the energy efficiency), due to the memory-wall bottleneck involving the frequent movement of data between the memory and the processing elements (PE). Computing-in-memory (CIM) is a promising candidate approach to breaking through this so-called memory wall bottleneck. SRAM cells provide unlimited endurance and compatibility with state-of-the-art logic processes. This paper outlines the background, trends, and challenges involved in the further development of SRAM-CIM macros. This paper also reviews recent silicon-verified SRAM-CIM macros designed for logic and multiplication-accumulation (MAC) operations. [ABSTRACT FROM AUTHOR]

The main focus of this paper is the presentation of reliable methods for the determination of the optimum coloring of a graph, commonly known in the literature as vertex coloring problem. It has been shown that networks of capacitively coupled oscillators can be used to solve vertex coloring problems. In this paper we address the negative impact of an unbalanced number of couplings for the oscillators on the performance of the network and compensate for this non-uniform coupling structure by an adjustment in the network itself. The negative effect of the memristor device-to-device variability of the NbOx memristor on the array functionality will be investigated and reduced via an adaptation of the memristor operating point. The main improvement in network performance is achieved by setting up a control procedure allowing the network to bypass the local solutions and converge to the global one. Two strategies inspired by global optimization algorithms will be proposed to allow the network to overcome sub-optimal solutions, and find the solution corresponding to the absolute minimum of a performance measure function of the vertex coloring problem. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

Walsh-Hadamard based orthogonal sampling of signals is studied in this paper, and an application of this technique is presented. Using orthogonal sampling, a single analog-to-digital converter (ADC) only is sufficient to perform parallel (simultaneous) recording from the sensors. Furthermore, employing Walsh functions as modulation signals, the required bandwidth of the ADC in the proposed system is equal to the bandwidth of a time-multiplexed ADC in a system with identical number of recording channels. Therefore, the bandwidth of the ADC in the proposed system is effectively employed and shared among all the channels. The efficient usage of the ADC bandwidth leads to saving power at the ADC stage and reducing the data-rate of the output signal compared to state-of-the-art recording systems based on frequency-division multiplexing. This paper presents the orthogonal sampling technique for neural recording in multi-channel recording systems which is implemented with four recording channels using a $0.18~\mu \text{m}$ technology which results in a power consumption of $1.26~\mu \text{W}$ /channel at a 0.8 V supply. [ABSTRACT FROM AUTHOR]

The series-paralleling technique of dc-dc converters is utilized in various domains of electrical engineering for improved power conversion. Previous studies have proposed and classified the control schemes for the series-paralleled converters. However, they have several restrictions and lack diversity. The purpose of this paper is to propose passivity-based control (PBC) for the diverse output series-parallel connection of dc-dc converters. It is proved that the output series-paralleled converters regulated by PBC are asymptotically stable. The output series-paralleled converters are numerically simulated to confirm the asymptotic stability. PBC is shown to maintain the stability of the output series-paralleled converters with diverse circuit topologies, parameters, and steady-states. The result of this paper theoretically supports the numerical and experimental considerations in the previous studies and justifies the further extension of the series-parallel connection. [ABSTRACT FROM AUTHOR]

This paper addresses and experimentally demonstrates a programmable linear equation solver by analog computation. A set of differential equations using transconductance devices directly translated from circuit theory converges to the linear equation solution. These energy-efficient analog techniques are experimentally demonstrated in a configurable analog platform. The resulting analog linear equation solution circuits are effectively analog filters. The paper analyzes the algorithmic issues and analog numerical analysis issues, including accuracy, convergence time, and the interpretation of condition number for analog solutions. [ABSTRACT FROM AUTHOR]

An energy-harvesting interface that incorporates a continuously scalable-conversion-ratio (CSCR) switched-capacitor (SC) dc-dc converter with maximum power point tracking (MPPT) is introduced in this paper. By exploiting unique characteristics of a CSCR SC converter, an MPPT based on the hill climbing algorithm is implemented with a real-time power monitoring scheme with a frequency-mapped sampling capacitor DAC, allowing variation-tolerant MPPT for various types of energy sources. The harvestable voltage range is significantly extended by a step-up/down convertible CSCR SC converter, and the voltage conversion efficiency is further improved with matrix-structured and load-mapped power switches. With the test chip fabricated in 28nm FDSOI technology, the proposed energy harvesting interface shows peak conversion efficiency of 89% and average efficiency of > 80% for 60-2, $794~\mu \text{W}$. MPPT efficiency of > 97 % is confirmed with energy harvesting measurement with a solar cell. [ABSTRACT FROM AUTHOR]

Setting retail electricity prices is one of the significant strategies for energy management of multi-microgrid (MMG) systems integrated with renewable energy. Nevertheless, the need of privacy preservation, the uncertainties of renewable energy and loads, as well as the time-varying scenarios, bring challenges for pricing problems. In this paper, a two-level pricing framework is proposed based on interval predictions and model-free reinforcement learning to address these challenges. In particular, at the higher level, the distribution system operator (DSO) is viewed as an agent, which sets retail electricity prices without detailed user information for privacy protection to maximize the total revenue from selling energy with reinforcement learning. For time-varying scenarios with intermittent photovoltaic power generation and diverse loads, a differentiable trust region layer is considered in reinforcement learning to improve the robustness of the policy updating process. While at the lower level, operators in microgrids solve three-phase unbalanced optimal power flow (OPF) problems to minimize generation cost and network power loss. Additionally, to deal with the challenges from the uncertainties of renewable power generation and user loads, interval predictions are chosen to quantify prediction errors and improve the flexibility of pricing policies. Finally, a set of experiments are conducted to validate the effectiveness of the proposed method for pricing problems in MMG systems. [ABSTRACT FROM AUTHOR]

With the advancement of technology, the size of transistors and the distance between them are reducing rapidly. Therefore, the critical charge of sensitive nodes is reducing, making SRAM cells, used for aerospace applications, more vulnerable to soft-error. If a radiation particle strikes a sensitive node of the standard 6T SRAM cell, the stored data in the cell are flipped, causing a single-event upset (SEU). Therefore, in this paper, a Soft-Error-Aware Read-Stability-Enhanced Low-Power 12T (SARP12T) SRAM cell is proposed to mitigate SEUs. To analyze the relative performance of SARP12T, it is compared with other recently published soft-error-aware SRAM cells, QUCCE12T, QUATRO12T, RHD12T, RHPD12T and RSP14T. All the sensitive nodes of SARP12T can regain their data even if the node values are flipped due to a radiation strike. Furthermore, SARP12T can recover from the effect of single-event multi-node upsets (SEMNUs) induced at its storage node-pair. Along with these advantages, the proposed cell exhibits the highest read stability, as the ‘0’-storing storage node, which is directly accessed by the bitline during read operation, can recover from any upset. Furthermore, SARP12T consumes the least hold power. SARP12T also exhibits higher write ability and shorter write delay than most of the comparison cells. All these improvements in the proposed cell are obtained by exhibiting only a slightly longer read delay and consuming slightly higher read and write energy. [ABSTRACT FROM AUTHOR]

This paper proposes a battery management system, including a fast battery charger, battery aging diagnosis, and charge estimation and balancing. The charger adopts a single-inductor single-input dual-output architecture to achieve charge balancing among battery cells. Interleaved pulse charging is proposed to reduce the charging time and slow down the aging process of batteries as well. This method also significantly suppresses the variations of the temperature of battery cells and is beneficial to the implementation of charge balancing. An artificial neural network is proposed to detect the state of health (SOH) of battery cells and improve the accuracy of the state of charge (SOC) estimation. The prototype is implemented in TSMC 0.35- $\mu \text{m}$ process and TensorFlow tools are used. Measurement results show that the interleaved pulse charging reduces 30% variation of the battery temperature and saves 24% charging time when charging four battery cells concurrently. A mean absolute error of SOC estimation of 0.35% is achieved in this work. [ABSTRACT FROM AUTHOR]

This paper explores a low standby power 10T (LP10T) SRAM cell with high read stability and write-ability (RSNM/WSNM/WM). The proposed LP10T SRAM cell uses a strong cross-coupled structure consisting standard inverter with a stacked transistor and Schmitt-trigger inverter with a double-length pull-up transistor. This along with the read path separated from true internal storage nodes eliminates the read-disturbance. Furthermore, it performs its write operation in pseudo differential form through write bitline and control signal with a write-assist technique. To estimate the proposed LP10T SRAM cell’s performance, it is compared with some state-of-the-art SRAM cells using HSPICE in 16-nm CMOS predictive technology model at 0.7 V supply voltage under harsh manufacturing process, voltage, and temperature variations. The proposed SRAM cell offers 4.65X/1.57X/1.46X improvement in RSNM/WSNM/WM and 4.40X/1.69X narrower spread in RSNM/WM compared to the conventional 6T SRAM cell. Furthermore, it shows 1.26X/1.08X/1.01X higher RSNM/WSNM/WM and 1.71X/1.25X tighter/wider spread in RSNM/WM compared to the best studied SRAM cells. The proposed SRAM cell indicates 74.48%/1.41% higher/lower read/write delay compared to the 6T SRAM cell. Moreover, it exhibits the third-(second-) best read (write) dynamic power, consuming 29.69% (26.87%) lower than the 6T SRAM cell. The leakage power is minimized by the proposed design, which is 37.35% and 12.08% lower than that of the 6T and best studied cells, respectively. Nonetheless, the proposed LP10T SRAM cell occupies 1.313X higher area compared to the 6T SRAM cell. [ABSTRACT FROM AUTHOR]

In-memory computing (IMC) is an effective solution for energy-efficient artificial intelligence applications. Analog IMC amortizes the power consumption of multiple sensing amplifiers with an analog-to-digital converter (ADC) and simultaneously completes the calculation of multi-line data with a high parallelism degree. Based on a universal one-transistor one-magnetic tunnel junction (MTJ) spin transfer torque magnetic RAM (STT-MRAM) cell, this paper demonstrates a novel tunneling magnetoresistance (TMR) ratio magnifying method to realize analog IMC. Previous concerns including low TMR ratio and analog calculation nonlinearity are addressed using device-circuit interaction. The TMR is magnified $7500\times $ using a latch structure in combination with the device. Peripheral circuits are minimally modified to enable in-memory matrix-vector multiplication. A current mirror with a feedback structure is implemented to enhance analog computing linearity and calculation accuracy. The proposed design maximumly supports 1024 2-bit input and 1-bit weight multiply-and-accumulate (MAC) computations simultaneously. The proposal is simulated using the 28-nm CMOS process and MTJ compact model. The integral nonlinearity is reduced by 57.6% compared with the conventional structure. 9.47-25.4 TOPS/W is realized with 2-bit input, 1-bit weight, and 4-bit output convolution neural network (CNN). [ABSTRACT FROM AUTHOR]

Fully passive sensors (FPS) are widely used as a simple and inexpensive alternative of their active counterparts in many applications. However, measurement of FPSs relies on extremely bulky and complicated reader circuitry. In some cases the readout method depends on measurement distance. In this paper, we propose a readout technique based on a two-mode LC-oscillator formed with two coupled resonators, i.e., the FPS and the LC tank in the reader. We show that such a configuration can offer robust measurement of fully passive resistive and capacitive sensors provided that the system operates under strong coupling. We examine measurement errors due to non-idealities of the system (finite coil $Q$ and frequency mismatch) and provide design guidelines to ameliorate these unwanted effects. Although applied to sensing in this work, this analysis can be extended to wireless power transfer and impedance transformation. [ABSTRACT FROM AUTHOR]

This paper proposes an analytical design procedure for a class of 2D filters with a directional characteristic in the frequency plane. The method starts from two types of low-pass prototypes, namely a Gaussian filter and a wide-band filter. The two prototypes and the resulted 2D filters are zero-phase, a very useful property in image processing tasks. The prototypes are parametric and can be scaled along the frequency axis, thus resulting narrower or wider. A 1D to 2D frequency mapping, based on accurate approximations, is derived and applied to each prototype, obtaining the desired directional 2D frequency response with specified selectivity and orientation. The designed filters are adjustable, with a frequency response in factored matrix form depending on specifications. The Gaussian prototype yields very selective directional filters, used in detecting oriented straight lines in images, as show simulation results on several test images. Using oriented wide-band filters, other useful 2D filters result, namely square and parallelogram filters. The designed filters have accurate frequency responses, good directional selectivity and high linearity at relatively low order. An advantage of this analytical approach is that the filter transfer function results in parametric form and allows for easy tunability. [ABSTRACT FROM AUTHOR]

This paper presents a high-precision bandgap voltage reference (BGR) with high-order temperature compensation. The compensation signal is generated by using both strong-inversion MOSFETs and Bipolar Junction transistors (BJTs), which cancels the high-order nonlinear term $T\ln (T)$ in the BJT base-emitter voltage (VBE), and thus a low temperature coefficient (TC) over a wide temperature range is achieved. The proposed BGR circuit is fabricated in a 0.18- $\mu \text{m}$ CMOS process with an active area of $0.256 m{m^{2}}$ and a max power consumption of 1.35 mW. A minimum TC of 0.706 ${\mathrm{ppm}}/{}^ \circ C$ from $- 25\,\,{}^ \circ C$ to 125 ${}^ \circ C$ is achieved after an 8-bit resistance trimming. The line sensitivity is 0.0146%/V operating from 3.2 V to 3.7 V. The BGR achieves a power supply rejection (PSR) of −63.4 dB and a noise spectrum density of $0.92 ~\mu \text{V}/\sqrt {Hz} $ at 10 Hz. [ABSTRACT FROM AUTHOR]

Ferroelectric field effect transistor (FeFET) memory has shown the potential to meet the requirements of the growing need for fast, dense, low-power, and non-volatile memories. In this paper, we propose a memory architecture named crossed-AND (C-AND), in which each storage cell consists of a single ferroelectric transistor. The write operation is performed using different write schemes and different absolute voltages, to account for the asymmetric switching voltages of the FeFET. It enables writing an entire wordline in two consecutive cycles and prevents current and power through the channel of the transistor. During the read operation, the current and power are mostly sensed at a single selected device in each column. The read scheme additionally enables reading an entire word without read errors, even along long bitlines. Our Simulations demonstrate that, in comparison to the previously proposed AND architecture, the C-AND architecture diminishes read errors, reduces write disturbs, enables the usage of longer bitlines, and saves up to 2.92X in memory cell area. [ABSTRACT FROM AUTHOR]

The stability criteria based on the Rollett parameter ($K$) plus one auxiliary condition is yet the most popular method used by designers to evaluate the stability of a two-port network although other criteria are available. In this paper, novel analytical proofs of the criteria utilizing $K$ and a new geometrical proof of the relationship between the stability of the ports of a circuit are proposed. They are obtained by making use of relationships between the reflection coefficients at the ports of the network and novel expressions that bind stability circles and unit mapping circles. The developed relations make the proposed proofs easier and more immediate than the traditional ones. Moreover, a new auxiliary condition having the same complexity and a greater geometrical insight with respect to the traditional ones is used. [ABSTRACT FROM AUTHOR]

In the Mesochronous Pipeline (MP), the clock period is the maximal of differences between the maximum/minimum delays of all stages. This value is less than the maximum delay between the memory elements, so MP is operating faster than the conventional pipeline (CP). To take full advantages of MP, in this paper, Automated Mesochronous Pipeline Scheduler (AMPS) for high performance digital circuits, is proposed which provides all allowed partitioning options generated by register allocation procedures obtained with varying maximum delay differences of stages. This allows a designer to select from design space produced by AMPS based on the desired specification. Unlike the traditional MP, AMPS utilizes automated scripts to extract the timing characteristics of the synchronizers and gates. This enables a designer to explore trade-offs between latency, power, and frequency. To evaluate our toolset, an 8-bit Carry Increment Adder (CIA), a 4-bit Binary-Coded Decimal (BCD) adder, a 4-bit ALU, and a set of circuits from ISCAS85 and ISCAS89 benchmarks are considered. All circuit level simulations are performed in the 65nm CMOS standard technology node. AMPS improves the frequency for its best solutions about 127.94% (on average) in comparison to the conventional pipeline scheme. [ABSTRACT FROM AUTHOR]

Sparse recovery algorithms are integral to compressed sensing (CS) as they facilitate the reconstruction of higher dimensional signals from sub-Nyquist measurements. Although Orthogonal Matching Pursuit (OMP) has been ubiquitously adopted in hardware implementations to curb the computational complexity of CS recovery, increasing sparsity levels incur higher reconstruction costs. Sparsity independent regularized pursuit (SIRP) is a recent algorithm that employs parallel index selection and regularization to curtail the number of iterations required to reconstruct the signal and thereby enhance the reconstruction speed. This paper proposes a novel reformulation of the SIRP algorithm from the hardware perspective, incorporating a cheaper regularization strategy and a modified Gram-Schmidt (MGS) based incremental QR decomposition (QRD) approach. The proposed design incorporates an iterative QRD architecture with feedback circuitry to exploit the parallelism of the triangularization step in MGS. Additionally, a fast inverse square block circumvents the need for parallel divider blocks giving considerable hardware and latency savings. The design reuses the iterative QRD block to implement the interdependent computations of the algorithm by sophisticated scheduling techniques. The proposed implementation on a Xilinx Virtex Ultrascale FPGA can recover 1024-dimensional signals from 25% measurements within $77~\mu \text{s}$ , which translates to a 37% reduction in processing cycles from state-of-art. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid precoding processor for millimeter wave (mmWave) multiple-input-multiple-output (MIMO) communication systems. The proposed architecture supports 64 antennas with 4-to-8 RF chains, and 4-bit phase resolution for each phase shifter in the analog beamformer. A polar decomposition (PD) engine is designed to achieve a 2.4-to- $8.6\times $ lower latency with 1.5-to- $1.7\times $ less normalized gate count compared to the singular value decomposition (SVD)-based implementation for the same functionality. Multiplicands are combined to reduce 55% area by utilizing the distributive property. Approximate phase extraction and low-precision multiplication for quantized phase extraction are adopted, which leads to 43% and 41% area reductions, respectively. The proposed hybrid precoding processor is designed in a 40-nm CMOS technology. It delivers a throughput of up to 10,288 kMat/s and consumes 170.9 mW at 278 MHz. It outperforms the state-of-the-art hybrid precoding processors with 5.5- $13.7\times $ higher normalized area efficiency and 6.9- $38.4\times $ lower normalized energy. [ABSTRACT FROM AUTHOR]

Recently, massive multiple-input multiple-output (MIMO) detection schemes based on message passing detection (MPD) have attracted extensive attention due to their good performance-complexity tradeoff. In this paper, to facilitate a high-throughput detector design, we introduce a layered updating schedule and propose an improved layered MPD (ILMPD) algorithm. In the new algorithm, several algorithmic transformations or approximations are derived for lower complexity. For instance, by exploiting the property of quadratic functions, the numbers of multiplications and additions in the constellation matching are both reduced by half; Through reasonable approximations, the multiplication, addition, and sorting operations in the initialization are all removed. Moreover, a lightweight early termination strategy is explored, reducing the number of detection iterations by nearly 20%. Based on the proposed ILMPD algorithm, an area-efficient architecture is devised, where several optimization methods are proposed for fewer resources and higher clock frequency. Compared with the state-of-the-art design, the presented ILMPD detector can deliver a nearly $3\times $ higher area efficiency. A reconfigurable version of the proposed detector has also been developed, which can well support modulations QPSK to 256-QAM and still exhibits a superior area efficiency. [ABSTRACT FROM AUTHOR]

The compressive sensing (CS) based sparse vector coding (SVC) method is one of the promising ways for the next-generation ultra-reliable and low-latency communications. In this paper, we present advanced algorithm-hardware co-optimization schemes for realizing a cost-effective SVC decoding architecture. The previous maximum a posteriori subspace pursuit (MAP-SP) algorithm is newly modified to relax the computational overheads by applying novel residual forwarding and LLR approximation schemes. A fully-pipelined parallel hardware is also developed to support the modified decoding algorithm, reducing the overall processing latency, especially at the support identification step. In addition, an advanced least-square-problem solver is presented by utilizing the parallel Cholesky decomposer design, further reducing the decoding latency with parallel updates of support values. The implementation results from a 22nm FinFET technology showed that the fully-optimized design is 9.6 times faster while improving the area efficiency by 12 times compared to the baseline realization. [ABSTRACT FROM AUTHOR]

Second order memristors are two terminal devices that present a conductance depending on two orders of variables, namely the geometric parameters and the internal temperature. They have shown to be able to mimic some specific features of neuron synapses, specifically Spike-Timing-Dependent-Plasticity (STDP), and consequently to be good candidates for neuromorphic computing. In particular, memristor crossbar structures appear to be suitable for implementing locally competitive algorithms and for tackling classification problems by exploiting temporal learning techniques. On the other hand, neuromorphic studies and experiments have revealed the existence of different kinds of plasticity and have shown the effect of calcium concentration on synaptic changes. Computational studies have investigated the behavior of spiking networks in the context of supervised, unsupervised, and reinforcement learning. In this paper, we first derive a simplified, almost analytical, model of a second-order memristor, only involving two variables, the mem-conductance, and the temperature, directly attributable to the synaptic efficacy and to the calcium concentration. Then we study in detail the response of a single memristive synapse to the most relevant plasticity models, including cycles of spike pairs, triplets, and quadruplets at different frequencies. Finally, we accurately characterize memristor spiking networks as discrete nonlinear dynamic systems, with mem-conductances as state variables and pre and postsynaptic spikes as inputs and outputs, respectively. The result shows that the model developed in this manuscript can explain and accurately reproduce a significant portion of observed synaptic behaviors, including those not captured by classical spike pair-based STDP models. Furthermore, under such an approach, the global dynamic behavior of memristor networks and the related learning mechanisms can be deeply analyzed by employing advanced nonlinear dynamic techniques. [ABSTRACT FROM AUTHOR]

The dynamics of modern hybrid power systems are characterized by behaviors at different timescales, typically separated by several orders of magnitude. In this paper we apply the envelope-following method to hybrid power systems simulations, in which single-phase and three-phase representations of a power system co-occur. This simulation method is well suited for handling coexisting behaviors occurring at different timescales. Compared to the simulation approaches present in the literature, the envelope-following method does not need to continuously switch between two separate simulation engines, but rather automatically handles instantaneous variations in the dynamics of the system, such as faults or topology changes. Additionally, given the vast penetration of power electronic components in modern electricity networks, employing the envelope-following method allows using a vast array of numerical algorithms that have been developed over the years to simulate electrical and electronic circuits. The proposed approach is validated by means of power systems case studies of increasing complexity. [ABSTRACT FROM AUTHOR]

This paper focuses on the synchronization control issue of networked systems. Due to the imperfect network environment, continuous or precise transmission is impractical, and intermittent communication scheme under state quantization needs to be considered. First, an easy-to-implement dynamic quantizer, which possesses finite quantization level, is designed to deal with the imprecise information sharing. Next, based on the designed quantizer, a dynamic estimator is introduced to estimate the real-time state information and generate control inputs. An event-based communication scheme is utilized to ensure that the estimate error does not exceed a certain threshold. Then, detailed design of the dynamically quantized controller is given to achieve exact synchronization, and corresponding distributed design is also provided to improve the feasibility. Moreover, some results for practical synchronization are derived. Finally, the validity of our theoretical results is illustrated by two numerical examples. [ABSTRACT FROM AUTHOR]

A random sampling-and-averaging (RSA) technique based on stochastic Monte Carlo methods is described in this paper for enhancing the accuracy of single-photon arrival-time measurements down to sub-picosecond ranges in emerging quantum applications. The theoretical variances of both synchronous and asynchronous RSA techniques are presented in the mathematical formats and experimentally verified by the Monte Carlo simulations. Meanwhile, the methodology of converting the mathematical models into an almost all-digital low-power integrated-circuit is elaborated by a circuit-level example with the instruction of setting circuit parameters. Along with the superior measurement resolution, scalable dynamic ranges, high linearity, high noise immunity, and low power/area consumption, the primary limitation of the RSA techniques has also been addressed for the forthcoming conversion-rate enhancement techniques. [ABSTRACT FROM AUTHOR]

In this paper, a new filtering power divider topology is presented. The divider is distinguished from traditional power dividers as it is capable of having a predefined transmission response, an improved isolation performance between the two output ports over a wide frequency range, and reflectionless responses at all three ports. Since the proposed divider topology is based on complete closed-form design equations derived through a rigorous mathematical analysis, it make it possible to design reflectionless filtering power dividers with any prespecified requirements without using time-consuming parametric optimizations. For verifying the proposed divider topology, we have designed, fabricated, and measured a microstrip Butterworth filtering power divider. The proposed design approach can also be applied to the design of filtering power dividers with other transmission responses such as Chebyshev and inverse-Chebyshev responses. [ABSTRACT FROM AUTHOR]

The trace of the controllability Gramian quantifies the average controllability in all directions in the system state space. In this paper, we investigate the average controllability of a semistable networked system with Laplacian dynamics and derive upper and lower bounds on the trace of its pseudo-controllability Gramian matrix. We show that these bounds are solely determined by the network topology, which can be obtained without computing any higher-dimensional matrix. We find that a sparse or a scale-free network is easy to control in terms of the average controllability. We then investigate the effect of the edges with negative weights on the average controllability for a signed network with Laplacian dynamics. We find that a small number of negatively-weighted edges can significantly affect the average controllability of the signed network. We finally demonstrate that many real-world networks are easy to control via manipulating negatively-weighted edges. [ABSTRACT FROM AUTHOR]

This paper presents a 46 nF/10 $\text{M}\Omega $ -range, digital-intensive, reconfigurable RC-to-digital converter (R2CDC) that can readout multiple C and R sensors in a time-interleaved fashion. Ratio-metric conversion using swing-boosted period-modulation (SB-PM) front-end by the R2CDC results in 114 aFrms/ $0.37 \Omega _{\text {rms}}$ resolutions and a worst-case temperature-drift of 64.2 ppm/°C over −40 to 125°C. Femto-farad capacitances can be sensed with a relative code-deviation less than 0.16 % even when parasitics vary 30 times the baseline. Implemented in a $0.18 ~\mu \text{m}$ standard CMOS process, the R2CDC consumes 140 $\mu \text{A}$ from a 1 V supply, occupying an active area of $0.175 ~\mu \text{m} ^{\mathrm{ 2}}$. [ABSTRACT FROM AUTHOR]

Multi-party computation (MPC) allows each peer to take part in the execution of a common function with their private share of data without the need to expose it to other participants. The Legendre symbol is a pseudo-random function (PRF) that is suitable for MPC protocols due to their efficient evaluation process compared to other symmetric primitives. Recently, Legendre-based PRFs have also been employed in the construction of a post-quantum signature scheme, namely LegRoast. In this paper, we propose, to the best of our knowledge, the first hardware implementations for the Legendre symbol by three approaches: 1) low-area, 2) high-speed, and 3) high-frequency. The high-speed architecture outperforms state-of-the-art software implementations, which run on Intel’s Core-i5. Our evaluation results on FPGA show that this architecture reduces the Legendre calculation time by $2.56\times $ compared to software implementations on Core-i5. On the other hand, the low-area architecture consumes only 5489 slices on the Artix-7 FPGA and is suitable for resource-constrained devices. Moreover, our ASIC implementation results indicate that the low-area architecture consumes 97.56K gates to implement and requires $4.01~mW$ to operate on 50 MHz. The high-frequency architecture increases the frequency by $1.72\times $ over the high-speed architecture and achieves 200 MHz frequency on FPGA. [ABSTRACT FROM AUTHOR]

Since chaotic cryptography has a long-term problem of dynamic degradation, this paper presents proof that chaotic systems resist dynamic degradation through theoretical analysis. Based on this proof, a novel one-dimensional two-parameter with a wide-range system mixed coupled map lattice model (TWMCML) is given. The evaluation of TWMCML shows that the system has the characteristics of strong chaos, high sensitivity, broader parameter ranges and wider chaos range, which helps to enhance the security of chaotic sequences. Based on the excellent performance of TWMCML, it is applied to the newly proposed encryption algorithm. The algorithm realizes double protection of private images under the premise of ensuring efficiency and safety. First, the important information of the image is extracted by edge detection technology. Then the important area is scrambled by the three-dimensional bit-level coupled XOR method. Finally, the global image is more fully confused by the dynamic index diffusion formula. The simulation experiment verified the effectiveness of the algorithm for grayscale and color images. Security tests show that the application of TWMCML makes the encryption algorithm have a better ability to overcome conventional attacks. [ABSTRACT FROM AUTHOR]

This paper investigates the formation control problem for linear multi-agent systems under switching directed topologies. Based on absolute or relative outputs, we propose two distributed observer-based event-triggered control schemes. Both schemes can guarantee the boundedness of formation errors under sufficient conditions. The schemes can also avoid Zeno behaviors by giving an estimation for the lower bound of sampling intervals. Finally, simulations and experiments validate the proposed approaches. [ABSTRACT FROM AUTHOR]

In a capacitive wireless power transfer system, due to the pF-level coupling capacitance, resonant inductors are commonly used at the primary side to boost voltage and at secondary side to boost the current. However, the inductors are bulky, in particular when it comes to integration, thereby not preferred by some space-sensitive applications like smartphones, smart glasses and headphones. This paper proposes a capacitive wireless power transfer architecture with no inductors at the receiver side. A high conversion ratio step down switched capacitor converter (SCC-CPT), rather than inductors, is used to compensate for the impedance mismatch between the secondary side of the capacitive coupler and the load. The analysis method and its associated design procedure of the SCC-CPT system is developed with the target of clarifying the tradeoffs among the secondary side efficiency, coupler voltage stress, receiver side integrability, and the primary side compensation network gain. Finally, a 7.5 W, 12 MHz SCC-CPT prototype was constructed to validate the proposed analysis and design. The measured system efficiency is 75.6 % at 7.5 W output, and the secondary side switched capacitor converter efficiency is 96.4 %. [ABSTRACT FROM AUTHOR]

This paper introduces the utilization of two different input-referred offset voltage correction methods applied to multiple amplifiers within a front-end sensing circuit of a buck regulator for the first time. The multi-amplifier system under investigation contains an instrumentation amplifier consisting of three folded cascode stages and an additional amplifier configured as a unity-gain buffer for a reference voltage. The first method in this work alleviates voltage offsets in this 4-amplifier system based on a shared auxiliary amplifier correction circuit that switches between different target amplifiers; whereas the second method applies a chopping-based auto-zero procedure to cancel the input-referred offset voltage of the same amplifiers. Since the instrumentation amplifier is designed for feedback sensing in integrated power management applications, it has a relatively high bandwidth requirement. For this reason, the chopping technique does not involve a low-pass or band-pass filter. Instead, a successive approximation register (SAR) analog-to-digital converter is used to sense the output. Measurements of the amplifiers fabricated in a 130nm CMOS technology demonstrate that the auxiliary auto-zero offset cancellation method leads to lower input-referred offset voltage standard deviation ($\sigma = 1.31\,\,\mu \text{V}$) compared to the chopping technique ($\sigma = 184.67\,\,\mu \text{V}$), and that the die area requirement and power consumption with the auxiliary amplifier-based offset cancellation (0.105 mm2, 1.32 mW) are lower than with the chopping method (0.25 mm2, 1.72 mW). [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]

Information exchanges among interacting agents play a significant role in guaranteeing successful completion of the desired coordinated control tasks for a multi-agent system (MAS). Furthermore, these information exchanges are often performed over some open and resource-constrained communication networks, thereby making security and resource efficiency vitally important for various multi-agent coordinated control problems. This paper addresses a secure dynamic event-trigger-based resilient consensus control problem for an MAS in the presence of both sensor and actuator attacks. First, a distributed adaptive compensator is introduced for prediction of unavailable system states. Due to the existence of sensor and actuator attack signals, a secure dynamic event-triggered mechanism is then proposed, and a resilient control strategy is further devised for the paralyzed MAS. It is theoretically proved that the controlled MAS is asymptotically stable and asymptotic consensus is eventually achieved among the coordinated agents regardless of the attacks and constrained resources. Finally, three examples are provided to illustrate the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

In this paper, the output feedback based periodic event-triggered control problem is considered for a class of nonminimum-phase nonlinear systems via dynamic event-triggering mechanism. When only the sampled-data output is known, a new periodic event-triggered control method is proposed via output feedback and the control input updates based on a discrete-time dynamic event-triggering condition. Despite the unstable zero dynamics, the proposed control method can asymptotically stabilize the hybrid control systems by closing the loop only when it is necessary. In contrast to the continuous-time static one, the proposed dynamic triggering condition has several advantages including the easier digital implementation and the larger average inter-event time interval. The delicate analysis gives the explicit expression of the maximum allowable sampling period, and the global asymptotic stability can be achieved for the hybrid control systems. Finally, the effectiveness of the proposed periodic event-triggered control method is verified by a numerical simulation. [ABSTRACT FROM AUTHOR]

This paper presents a discrete-time low-noise amplifier for miniaturized sensor nodes. Such amplifier achieves a noise efficiency factor of 1.01 and a power efficiency factor of 1.63, improving the noise efficiency of an analog front-end. The proposed preamplifier employs discrete-time parametric amplification by modulating the capacitance of a MOS varactor. The sampling noise is minimized by adopting a high oversampling ratio of the input voltage, leading to an input-referred noise of 520 nVrms in a 1 kHz bandwidth. Also, the power consumption is reduced by using a 34-phase stepwise charging technique. The multiphase soft-charging technique is implemented using multiple time-interleaved cells and allows to significantly reduce the current consumption without introducing any significant penalty in terms of additional noise or area occupation. A two-stage parametric amplifier is introduced to provide higher gain, and suppress the noise contribution of the following amplifier chain. The proposed two-stage complementary preamplifier is followed by a third continuous-time stage, that employs a current-reuse inverter-based architecture. The contribution to the total noise efficiency factor of such amplifier is attenuated by the gain of the previous two-stage parametric preamplifier. As a result, the third stage provides gain programmability, while degrading the NEF of the entire chain by less than 10%. [ABSTRACT FROM AUTHOR]

This paper proposes a fingerprint scanning analog front-end (AFE) for a $41\times32$ under-glass mutual-capacitive fingerprint sensor. As the mutual-capacitive fingerprint sensor is a smaller version of a projected-capacitive touch screen, this transparent fingerprint sensor can be mounted underneath screen cover glass. However, such glass significantly diminishes the signal-to-noise ratio (SNR) of fingerprint scanning AFE in proportion with increasing glass thickness. Moreover, external noise interferences from the display and charger are severe in displays with thin form factor. The proposed fingerprint scanning AFE can achieve a 17 atto-farad capacitance resolution using high-voltage (20 V) transmitters and multi-channel receivers comprising a pipelined readout amplifier, mixer, and second-order low-pass filter (128 kHz). A differential sensing structure and band-pass filtering are employed in the receiver front-end to enhance the noise immunity. A differential phase-encoded sequential driving transmitter with a proposed on-chip replica channel mitigate random offsets in the readout amplifier with high matching accuracy. Measurement results show that the fingerprint scanning integrated circuit (IC) fabricated by a $0.18 ~\mu \text{m}$ BCD (Bipolar-CMOS-DMOS) process achieved a 13.4 dB SNR at a frame rate of 120 Hz under a 0.2 mm-thick cover glass. The prototype IC provides 20 VPP noise immunity from 0 to 500 kHz and consumes 23.2 mW from a 3.3 V supply. [ABSTRACT FROM AUTHOR]

In this paper, a single-channel two-step voltage-time hybrid domain analog-to-digital converter (ADC) is proposed. To achieve high sampling rate and high accuracy, 3.5-bit voltage domain MDAC and 7-bit high-speed time domain ADC (TD-ADC) are combined into a 10-bit hybrid ADC. In the first stage MDAC, a low-power push-pull amplifier is used to improve settling speed, and 1-bit redundancy is designed for calibration and dither injection. The TD-ADC with selective time domain quantization is implemented by a constant-current voltage to time converter (VTC) array and a direct positive feedback time domain comparator. The proposed VTC array can maintain high linearity with a large input swing in high-speed application. The prototype ADC was fabricated in a 28-nm CMOS process and occupied a core area of 0.074 mm2. Under a 0.95-V power supply, the chip achieves a measured peak SNDR of 53.2 dB and SFDR of 61.7 dB respectively at conversion rate up to 2.5 GS/s. The FOM is 48.2 fJ/conversion-step. [ABSTRACT FROM AUTHOR]

This paper presents a time-interleaved analog-to-digital converter mismatch calibration, which can be adapted to any interleaving factor and calibrate offset, gain, time skew and bandwidth mismatches. Moreover, the proposed scheme utilizes a non-return-to-zero pilot signal, avoiding the high-resolution requirements of sinusoidal generators. In addition, there are no multiplications in the detection path in contrast with the state-of-the-art approaches. Consequently, the proposed calibration presents the smallest complexity in terms of hardware requirements compared to previous work (to the author’s knowledge). Therefore, the proposed calibration can be made with the smallest area and power consumption. Numerical simulations show an SNDR improvement of 49.52 dB for a 12-bit 4-channel TI-ADC and an SNDR improvement of 29.64 dB for an 8-bit 32-channel TI-ADC when random mismatches are applied. The measurement results show an SNDR improvement of 24.68 dB for a 3.6 GS/s 2-channel TI-ADC experimental board. [ABSTRACT FROM AUTHOR]