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  Subjects -> ELECTRONICS (Total: 207 journals)
Showing 1 - 200 of 277 Journals sorted alphabetically
Acta Electronica Malaysia     Open Access  
Advanced Materials Technologies     Hybrid Journal   (Followers: 1)
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 8)
Advances in Electrical and Electronic Engineering     Open Access   (Followers: 9)
Advances in Electronics     Open Access   (Followers: 100)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 8)
Advances in Microelectronic Engineering     Open Access   (Followers: 13)
Advances in Power Electronics     Open Access   (Followers: 41)
Advancing Microelectronics     Hybrid Journal  
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 28)
Annals of Telecommunications     Hybrid Journal   (Followers: 8)
APSIPA Transactions on Signal and Information Processing     Open Access   (Followers: 8)
Archives of Electrical Engineering     Open Access   (Followers: 16)
Australian Journal of Electrical and Electronics Engineering     Hybrid Journal  
Batteries     Open Access   (Followers: 9)
Batteries & Supercaps     Hybrid Journal   (Followers: 5)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 31)
Bioelectronics in Medicine     Hybrid Journal  
Biomedical Instrumentation & Technology     Hybrid Journal   (Followers: 6)
BULLETIN of National Technical University of Ukraine. Series RADIOTECHNIQUE. RADIOAPPARATUS BUILDING     Open Access   (Followers: 2)
Bulletin of the Polish Academy of Sciences : Technical Sciences     Open Access   (Followers: 1)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 47)
China Communications     Full-text available via subscription   (Followers: 9)
Chinese Journal of Electronics     Hybrid Journal  
Circuits and Systems     Open Access   (Followers: 15)
Consumer Electronics Times     Open Access   (Followers: 5)
Control Systems     Hybrid Journal   (Followers: 308)
ECTI Transactions on Computer and Information Technology (ECTI-CIT)     Open Access  
ECTI Transactions on Electrical Engineering, Electronics, and Communications     Open Access   (Followers: 2)
Edu Elektrika Journal     Open Access   (Followers: 1)
Electrica     Open Access  
Electronic Design     Partially Free   (Followers: 124)
Electronic Markets     Hybrid Journal   (Followers: 7)
Electronic Materials Letters     Hybrid Journal   (Followers: 4)
Electronics     Open Access   (Followers: 109)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 10)
Electronics For You     Partially Free   (Followers: 103)
Electronics Letters     Hybrid Journal   (Followers: 26)
Elektronika ir Elektortechnika     Open Access   (Followers: 2)
Elkha : Jurnal Teknik Elektro     Open Access  
Emitor : Jurnal Teknik Elektro     Open Access   (Followers: 3)
Energy Harvesting and Systems     Hybrid Journal   (Followers: 4)
Energy Storage     Hybrid Journal   (Followers: 1)
Energy Storage Materials     Full-text available via subscription   (Followers: 4)
EPE Journal : European Power Electronics and Drives     Hybrid Journal  
EPJ Quantum Technology     Open Access   (Followers: 1)
EURASIP Journal on Embedded Systems     Open Access   (Followers: 11)
Facta Universitatis, Series : Electronics and Energetics     Open Access  
Foundations and Trends® in Communications and Information Theory     Full-text available via subscription   (Followers: 6)
Foundations and Trends® in Signal Processing     Full-text available via subscription   (Followers: 9)
Frequenz     Hybrid Journal   (Followers: 1)
Frontiers of Optoelectronics     Hybrid Journal   (Followers: 1)
IACR Transactions on Symmetric Cryptology     Open Access   (Followers: 1)
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 103)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 81)
IEEE Embedded Systems Letters     Hybrid Journal   (Followers: 57)
IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology     Hybrid Journal   (Followers: 3)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 52)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 9)
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits     Hybrid Journal   (Followers: 1)
IEEE Letters on Electromagnetic Compatibility Practice and Applications     Hybrid Journal   (Followers: 4)
IEEE Magnetics Letters     Hybrid Journal   (Followers: 7)
IEEE Nanotechnology Magazine     Hybrid Journal   (Followers: 42)
IEEE Open Journal of Circuits and Systems     Open Access   (Followers: 3)
IEEE Open Journal of Industry Applications     Open Access   (Followers: 3)
IEEE Open Journal of the Industrial Electronics Society     Open Access   (Followers: 3)
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 77)
IEEE Pulse     Hybrid Journal   (Followers: 5)
IEEE Reviews in Biomedical Engineering     Hybrid Journal   (Followers: 23)
IEEE Solid-State Circuits Letters     Hybrid Journal   (Followers: 3)
IEEE Solid-State Circuits Magazine     Hybrid Journal   (Followers: 13)
IEEE Transactions on Aerospace and Electronic Systems     Hybrid Journal   (Followers: 368)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 74)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 64)
IEEE Transactions on Autonomous Mental Development     Hybrid Journal   (Followers: 8)
IEEE Transactions on Biomedical Engineering     Hybrid Journal   (Followers: 39)
IEEE Transactions on Broadcasting     Hybrid Journal   (Followers: 13)
IEEE Transactions on Circuits and Systems for Video Technology     Hybrid Journal   (Followers: 26)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 46)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 19)
IEEE Transactions on Geoscience and Remote Sensing     Hybrid Journal   (Followers: 228)
IEEE Transactions on Haptics     Hybrid Journal   (Followers: 5)
IEEE Transactions on Industrial Electronics     Hybrid Journal   (Followers: 75)
IEEE Transactions on Industry Applications     Hybrid Journal   (Followers: 40)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 27)
IEEE Transactions on Learning Technologies     Full-text available via subscription   (Followers: 12)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 80)
IEEE Transactions on Services Computing     Hybrid Journal   (Followers: 4)
IEEE Transactions on Signal and Information Processing over Networks     Hybrid Journal   (Followers: 13)
IEEE Transactions on Software Engineering     Hybrid Journal   (Followers: 79)
IEEE Women in Engineering Magazine     Hybrid Journal   (Followers: 11)
IEEE/OSA Journal of Optical Communications and Networking     Hybrid Journal   (Followers: 16)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 12)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 5)
IET Cyber-Physical Systems : Theory & Applications     Open Access   (Followers: 1)
IET Energy Systems Integration     Open Access   (Followers: 1)
IET Microwaves, Antennas & Propagation     Hybrid Journal   (Followers: 36)
IET Nanodielectrics     Open Access  
IET Power Electronics     Hybrid Journal   (Followers: 61)
IET Smart Grid     Open Access   (Followers: 1)
IET Wireless Sensor Systems     Hybrid Journal   (Followers: 18)
IETE Journal of Education     Open Access   (Followers: 4)
IETE Journal of Research     Open Access   (Followers: 11)
IETE Technical Review     Open Access   (Followers: 13)
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)     Open Access   (Followers: 3)
Industrial Technology Research Journal Phranakhon Rajabhat University     Open Access  
Informatik-Spektrum     Hybrid Journal   (Followers: 2)
Instabilities in Silicon Devices     Full-text available via subscription   (Followers: 1)
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 14)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 18)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 12)
International Journal of Antennas and Propagation     Open Access   (Followers: 11)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 4)
International Journal of Computational Vision and Robotics     Hybrid Journal   (Followers: 5)
International Journal of Control     Hybrid Journal   (Followers: 11)
International Journal of Electronics     Hybrid Journal   (Followers: 7)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 13)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal   (Followers: 3)
International Journal of High Speed Electronics and Systems     Hybrid Journal  
International Journal of Hybrid Intelligence     Hybrid Journal  
International Journal of Image, Graphics and Signal Processing     Open Access   (Followers: 16)
International Journal of Microwave and Wireless Technologies     Hybrid Journal   (Followers: 10)
International Journal of Nanoscience     Hybrid Journal   (Followers: 1)
International Journal of Numerical Modelling: Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 4)
International Journal of Power Electronics     Hybrid Journal   (Followers: 25)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 4)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 10)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 4)
International Journal of Wireless and Microwave Technologies     Open Access   (Followers: 6)
International Transaction of Electrical and Computer Engineers System     Open Access   (Followers: 2)
JAREE (Journal on Advanced Research in Electrical Engineering)     Open Access  
Journal of Biosensors & Bioelectronics     Open Access   (Followers: 4)
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Journal of Artificial Intelligence     Open Access   (Followers: 12)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 4)
Journal of Computational Intelligence and Electronic Systems     Full-text available via subscription   (Followers: 1)
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 38)
Journal of Electrical Bioimpedance     Open Access  
Journal of Electrical Bioimpedance     Open Access   (Followers: 2)
Journal of Electrical Engineering & Electronic Technology     Hybrid Journal   (Followers: 7)
Journal of Electrical, Electronics and Informatics     Open Access  
Journal of Electromagnetic Analysis and Applications     Open Access   (Followers: 8)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (Followers: 9)
Journal of Electronic Design Technology     Full-text available via subscription   (Followers: 6)
Journal of Electronic Science and Technology     Open Access   (Followers: 1)
Journal of Electronics (China)     Hybrid Journal   (Followers: 5)
Journal of Energy Storage     Full-text available via subscription   (Followers: 4)
Journal of Engineered Fibers and Fabrics     Open Access   (Followers: 2)
Journal of Field Robotics     Hybrid Journal   (Followers: 4)
Journal of Guidance, Control, and Dynamics     Hybrid Journal   (Followers: 190)
Journal of Information and Telecommunication     Open Access   (Followers: 1)
Journal of Intelligent Procedures in Electrical Technology     Open Access   (Followers: 3)
Journal of Low Power Electronics     Full-text available via subscription   (Followers: 10)
Journal of Low Power Electronics and Applications     Open Access   (Followers: 10)
Journal of Microelectronics and Electronic Packaging     Hybrid Journal   (Followers: 1)
Journal of Microwave Power and Electromagnetic Energy     Hybrid Journal   (Followers: 3)
Journal of Microwaves, Optoelectronics and Electromagnetic Applications     Open Access   (Followers: 11)
Journal of Nuclear Cardiology     Hybrid Journal  
Journal of Optoelectronics Engineering     Open Access   (Followers: 4)
Journal of Physics B: Atomic, Molecular and Optical Physics     Hybrid Journal   (Followers: 32)
Journal of Power Electronics     Hybrid Journal   (Followers: 2)
Journal of Power Electronics & Power Systems     Full-text available via subscription   (Followers: 11)
Journal of Semiconductors     Full-text available via subscription   (Followers: 5)
Journal of Sensors     Open Access   (Followers: 27)
Journal of Signal and Information Processing     Open Access   (Followers: 8)
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer     Open Access  
Jurnal Rekayasa Elektrika     Open Access  
Jurnal Teknik Elektro     Open Access  
Jurnal Teknologi Elektro     Open Access  
Kinetik : Game Technology, Information System, Computer Network, Computing, Electronics, and Control     Open Access  
Majalah Ilmiah Teknologi Elektro : Journal of Electrical Technology     Open Access   (Followers: 2)
Metrology and Measurement Systems     Open Access   (Followers: 6)
Microelectronics and Solid State Electronics     Open Access   (Followers: 28)
Nanotechnology, Science and Applications     Open Access   (Followers: 6)
Nature Electronics     Hybrid Journal   (Followers: 1)
Networks: an International Journal     Hybrid Journal   (Followers: 5)
Open Electrical & Electronic Engineering Journal     Open Access  
Open Journal of Antennas and Propagation     Open Access   (Followers: 8)
Paladyn. Journal of Behavioral Robotics     Open Access   (Followers: 1)
Power Electronics and Drives     Open Access   (Followers: 2)
Problemy Peredachi Informatsii     Full-text available via subscription  
Progress in Quantum Electronics     Full-text available via subscription   (Followers: 7)
Radiophysics and Quantum Electronics     Hybrid Journal   (Followers: 2)
Recent Advances in Communications and Networking Technology     Hybrid Journal   (Followers: 3)
Recent Advances in Electrical & Electronic Engineering     Hybrid Journal   (Followers: 11)
Research & Reviews : Journal of Embedded System & Applications     Full-text available via subscription   (Followers: 6)
Revue Méditerranéenne des Télécommunications     Open Access  
Security and Communication Networks     Hybrid Journal   (Followers: 2)
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     Hybrid Journal   (Followers: 57)
Semiconductors and Semimetals     Full-text available via subscription   (Followers: 1)
Sensing and Imaging : An International Journal     Hybrid Journal   (Followers: 2)
Solid State Electronics Letters     Open Access  
Solid-State Electronics     Hybrid Journal   (Followers: 9)
Superconductor Science and Technology     Hybrid Journal   (Followers: 3)
Synthesis Lectures on Power Electronics     Full-text available via subscription   (Followers: 3)
Technical Report Electronics and Computer Engineering     Open Access  
TELE     Open Access  
Telematique     Open Access  
TELKOMNIKA (Telecommunication, Computing, Electronics and Control)     Open Access   (Followers: 9)
Transactions on Cryptographic Hardware and Embedded Systems     Open Access   (Followers: 2)

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Similar Journals
Journal Cover
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Number of Followers: 1  
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Online) 2329-9231
Published by IEEE Homepage  [229 journals]
  • Special Topic on Exploratory Devices and Circuits for Compute-in-Memory
    • Authors: Shimeng Yu;
      Abstract: Deep learning and nonconvex optimization problems are well known data-intensive applications. Although graphic processing units (GPUs) have become the mainstream platform to accelerate the algorithms in the cloud, there is a growing interest to develop application-specific integrated-circuit (ASIC) chips for further improving the energy-efficiency for these data-intensive workloads. Digital multiply-and-accumulate (MAC) arrays are generally employed as ASIC solutions, and data flow is often optimized to increase the data reuse on-chip. Nevertheless, most of the inputs and outputs are moved across MAC arrays and from global buffers. Therefore, it is more attractive to embed the MAC computations into the memory array itself, namely compute-in-memory (CIM), to minimize the data transfer. In CIM, the vector–matrix multiplication is executed in parallel (with analog computation) where the input vectors activate multiple rows. The dot-product is obtained as the multiplication of input voltage and cell conductance, and the partial sum is added up by the column current. An analog-to-digital converter (ADC) at the edge of the array generally converts the partial sum to binary bits for further digital processing.
      PubDate: June 2020
      Issue No: Vol. 6, No. 1 (2020)
  • Energy-Efficient Moderate Precision Time-Domain Mixed-Signal
           Vector-by-Matrix Multiplier Exploiting 1T-1R Arrays
    • Authors: Shubham Sahay;Mohammad Bavandpour;Mohammad Reza Mahmoodi;Dmitri Strukov;
      Pages: 18 - 26
      Abstract: The emerging mobile devices in the era of Internet-of-Things (IoT) require a dedicated processor to enable computationally intensive applications such as neuromorphic computing and signal processing. Vector-by-matrix multiplication is the most prominent operation in these applications. Therefore, there is a critical need for compact and ultralow-power vector-by-matrix multiplier (VMM) blocks to perform resource-intensive low-to-moderate precision computations. To this end, in this article, we propose a time-domain mixed-signal VMM exploiting a modified configuration of 1MOSFET-1RRAM (1T-1R) array. The proposed VMM overcomes the energy inefficiency of the current-mode VMM approaches based on RRAMs. A rigorous analysis of different nonideal factors affecting the computational precision indicates that the nonnegligible minimum cell currents, channel length modulation (CLM), and drain-induced barrier lowering (DIBL) are the dominant mechanisms degrading the precision of the proposed VMM. We also show that there exists a tradeoff between the computational precision, dynamic range, and the area- and energy-efficiency of the proposed VMM approach. Therefore, we provide the necessary design guidelines for optimizing the performance. Our preliminary results indicate that an effective computational precision of 6 bits is achievable owing to the inherent compensation effect in the modified 1T-1R blocks. Furthermore, a 4-bit $200times200$ VMM utilizing the proposed approach exhibits a significantly high energy efficiency of ~1.5 Pops/J and a throughput of 2.5 Tops/s including the contribution from the input/output (I/O) circuitry.
      PubDate: June 2020
      Issue No: Vol. 6, No. 1 (2020)
  • Accurate Inference With Inaccurate RRAM Devices: A Joint Algorithm-Design
    • Authors: Gouranga Charan;Abinash Mohanty;Xiaocong Du;Gokul Krishnan;Rajiv V. Joshi;Yu Cao;
      Pages: 27 - 35
      Abstract: Resistive random access memory (RRAM) is a promising technology for energy-efficient neuromorphic accelerators. However, when a pretrained deep neural network (DNN) model is programmed to an RRAM array for inference, the model suffers from accuracy degradation due to RRAM nonidealities, such as device variations, quantization error, and stuck-at-faults. Previous solutions involving multiple read–verify–write (R-V-W) to the RRAM cells require cell-by-cell compensation and, thus, an excessive amount of processing time. In this article, we propose a joint algorithm-design solution to mitigate the accuracy degradation. We first leverage knowledge distillation (KD), where the model is trained with the RRAM nonidealities to increase the robustness of the model under device variations. Furthermore, we propose random sparse adaptation (RSA), which integrates a small on-chip memory with the main RRAM array for postmapping adaptation. Only the on-chip memory is updated to recover the inference accuracy. The joint algorithm-design solution achieves the state-of-the-art accuracy of 99.41% for MNIST (LeNet-5) and 91.86% for CIFAR-10 (VGG-16) with up to 5% parameters as overhead while providing a 15– $150times $ speedup compared with R-V-W.
      PubDate: June 2020
      Issue No: Vol. 6, No. 1 (2020)
  • A Fully Integrated Reprogrammable CMOS-RRAM Compute-in-Memory Coprocessor
           for Neuromorphic Applications
    • Authors: Justin M. Correll;Vishishtha Bothra;Fuxi Cai;Yong Lim;Seung Hwan Lee;Seungjong Lee;Wei D. Lu;Zhengya Zhang;Michael P. Flynn;
      Pages: 36 - 44
      Abstract: Analog compute-in-memory with resistive random access memory (RRAM) devices promises to overcome the data movement bottleneck in data-intensive artificial intelligence (AI) and machine learning. RRAM crossbar arrays improve the efficiency of vector-matrix multiplications (VMMs), which is a vital operation in these applications. The prototype IC is the first complete, fully integrated analog-RRAM CMOS coprocessor. This article focuses on the digital and analog circuitry that supports efficient and flexible RRAM-based computation. A passive $54times108$ RRAM crossbar array performs VMM in the analog domain. Specialized mixed-signal circuits stimulate and read the outputs of the RRAM crossbar. The single-chip CMOS prototype includes a reduced instruction set computer (RISC) processor interfaced to a memory-mapped mixed-signal core. In the mixed-signal core, ADCs and DACs interface with the passive RRAM crossbar. The RISC processor controls the mixed-signal circuits and the algorithm data path. The system is fully programmable and supports forward and backward propagation. As proof of concept, a fully integrated 0.18- $mu text{m}$ CMOS prototype with a postprocessed RRAM array demonstrates several key functions of machine learning, including online learning. The mixed-signal core consumes 64 mW at an operating frequency of 148 MHz. The total system power consumption considering the mixed-signal circuitry, the digital processor, and the passive RRAM array is 307 mW. The maximum theoretical throughput is 2.6 GOPS at an efficiency of 8.5 GOPS/W.
      PubDate: June 2020
      Issue No: Vol. 6, No. 1 (2020)
  • A Relaxed Quantization Training Method for Hardware Limitations of
           Resistive Random Access Memory (ReRAM)-Based Computing-in-Memory
    • Authors: Wei-Chen Wei;Chuan-Jia Jhang;Yi-Ren Chen;Cheng-Xin Xue;Syuan-Hao Sie;Jye-Luen Lee;Hao-Wen Kuo;Chih-Cheng Lu;Meng-Fan Chang;Kea-Tiong Tang;
      Pages: 45 - 52
      Abstract: Nonvolatile computing-in-memory (nvCIM) exhibits high potential for neuromorphic computing involving massive parallel computations and for achieving high energy efficiency. nvCIM is especially suitable for deep neural networks, which are required to perform large amounts of matrix–vector multiplications. However, a comprehensive quantization algorithm has yet to be developed, which overcomes the hardware limitations of resistive random access memory (ReRAM)-based nvCIM, such as the number of I/O, word lines (WLs), and ADC outputs. In this article, we propose a quantization training method for compressing deep models. The method comprises three steps: input and weight quantization, ReRAM convolution (ReConv), and ADC quantization. ADC quantization optimizes the error sampling problem by using the Gumbel-softmax trick. Under a 4-bit ADC of nvCIM, the accuracy only decreases by 0.05% and 1.31% for the MNIST and CIFAR-10, respectively, compared with the corresponding accuracies obtained under an ideal ADC. The experimental results indicate that the proposed method is effective for compensating the hardware limitations of nvCIM macros.
      PubDate: June 2020
      Issue No: Vol. 6, No. 1 (2020)
  • Memristor-Based Analog Recursive Computation Circuit for Linear
           Programming Optimization
    • Authors: Liuting Shang;Muhammad Adil;Ramtin Madani;Chenyun Pan;
      Pages: 53 - 61
      Abstract: Linear programming optimization is central to engineering designs, logistics management, and decision-making in every sector of the economy. Traditional hardware using CPU and GPU platforms for this purpose is severely limited by the scaling of the transistor technology. In this article, we design an analog in-memory computation circuit for accelerating linear programming optimization problems. The scheme includes a memristive crossbar array and analog peripheral circuits that do not require DAC/ADC between each algorithm iteration. In addition, several key parameters related to nonideal device characteristics and interconnect parasitics are discussed for providing practical guidelines. Furthermore, three design schemes are proposed to alleviate the computation error caused by the interconnect resistance for a large-scale crossbar array implementation. Optimal design parameters are quantified under a given number of array size and memristive resistance. Finally, the proposed hardware accelerator and error mitigation techniques are applied to six real-world power system optimization problems. The results show that the average error of generator power and the overall cost is less than 3%. It is demonstrated that the proposed accelerator achieves area, delay, and energy consumption reductions of $sim 151times $ , $sim 33times $ , and $sim 21times $ , respectively, compared with the CMOS digital circuits at the 16-nm technology node for a $1000times1000$ array with 6-bit precision.
      PubDate: June 2020
      Issue No: Vol. 6, No. 1 (2020)
  • Short-Term Long-Term Compute-in-Memory Architecture: A Hybrid Spin/CMOS
           Approach Supporting Intrinsic Consolidation
    • Authors: Shadi Sheikhfaal;Ronald F. Demara;
      Pages: 62 - 70
      Abstract: Biological memory structures impart enormous retention capacity while automatically providing vital functions for chronological information management and update the resolution of the domain and episodic knowledge. A crucial requirement for hardware realization of such cortical operations found in biology is to first design both short-term memory (STM) and long-term memory (LTM). Herein, these memory features are realized via a beyond-CMOS-based learning approach derived from the repeated input information and retrieval of the encoded data. We first propose a new binary STM-LTM architecture with composite synapse of the spin Hall effect-driven magnetic tunnel junction (SHE-MTJ) and capacitive memory bit cell to mimic the behavior of biological synapses. This STM-LTM platform realizes the memory potentiation through a continual update process using STM-to-LTM transfer, which is applied to neural networks based on the established capacitive crossbar. We then propose a hardware-enabled and customized STM-LTM transition algorithm for the platform considering the real hardware parameters. We validate the functionality of the design using SPICE simulations that show the proposed synapse has the potential of reaching ~30.2-pJ energy consumption for STM-to-LTM transfer and 65 pJ during STM programming. We further analyze the correlation between energy, array size, and STM-to-LTM threshold utilizing the MNIST data set.
      PubDate: June 2020
      Issue No: Vol. 6, No. 1 (2020)
  • Analyzing the Effects of Interconnect Parasitics in the STT CRAM In-Memory
           Computational Platform
    • Authors: Masoud Zabihi;Arvind K. Sharma;Meghna G. Mankalale;Zamshed Iqbal Chowdhury;Zhengyang Zhao;Salonik Resch;Ulya R. Karpuzcu;Jian-Ping Wang;Sachin S. Sapatnekar;
      Pages: 71 - 79
      Abstract: This article presents a method for analyzing the parasitic effects of interconnects on the performance of the STT-MTJ-based computational random access memory (CRAM) in-memory computation platform. The CRAM is a platform that makes a small reconfiguration to a standard spintronics-based memory array to enable logic operations within the array. The analytical method in this article develops a methodology that quantifies the way in which wire parasitics limit the size and configuration of a CRAM array and studies the impact of cell- and array-level design choices on the CRAM noise margin. Finally, the method determines the maximum allowable CRAM array size under various technology considerations.
      PubDate: June 2020
      Issue No: Vol. 6, No. 1 (2020)
  • A DNA Read Alignment Accelerator Based on Computational RAM
    • Authors: Zamshed I. Chowdhury;Masoud Zabihi;S. Karen Khatamifard;Zhengyang Zhao;Salonik Resch;Meisam Razaviyayn;Jian-Ping Wang;Sachin S. Sapatnekar;Ulya R. Karpuzcu;
      Pages: 80 - 88
      Abstract: Recent years have witnessed an increasing interest in the processing-in-memory (PIM) paradigm in computing due to its promise to improve the performance through the reduction of energy-hungry and long-latency memory accesses. Joined with the explosion of data to be processed, produced in genomics—particularly genome sequencing—PIM has become a potential promising candidate for accelerating genomics applications since they do not scale up well in conventional von Neumann systems. In this article, we present an in-memory accelerator architecture for DNA read alignment. This architecture outperforms corresponding software implementation by >49X and >18 000X, in terms of throughput and energy efficiency, respectively, even under conservative assumptions.
      PubDate: June 2020
      Issue No: Vol. 6, No. 1 (2020)
  • A Spiking Recurrent Neural Network With Phase-Change Memory Neurons and
    • Authors: Giacomo Pedretti;Piergiulio Mannocci;Shahin Hashemkhani;Valerio Milo;Octavian Melnic;Elisabetta Chicca;Daniele Ielmini;
      Pages: 89 - 97
      Abstract: Data-intensive computing applications, such as object recognition, time series prediction, and optimization tasks, are becoming increasingly important in several fields, including smart mobility, health, and industry. Because of the large amount of data involved in the computation, the conventional von Neumann architecture suffers from excessive latency and energy consumption due to the memory bottleneck. A more efficient approach consists of in-memory computing (IMC), where computational operations are directly carried out within the data. IMC can take advantage of the rich physics of memory devices, such as their ability to store analog values to be used in matrix–vector multiplication (MVM) and their stochasticity that is highly valuable in the frame of optimization and constraint satisfaction problems (CSPs). This article presents a stochastic spiking neuron based on a phase-change memory (PCM) device for the solution of CSPs within a Hopfield recurrent neural network (RNN). In the RNN, the PCM cell is used as the integrating element of a stochastic neuron, supporting the solution of a typical CSP, namely a Sudoku puzzle in hardware. Finally, the ability to solve Sudoku puzzles using RNNs with PCM-based neurons is studied for increasing size of Sudoku puzzles by a compact simulation model, thus supporting our PCM-based RNN for data-intensive computing.
      PubDate: June 2020
      Issue No: Vol. 6, No. 1 (2020)
  • aCortex: An Energy-Efficient Multipurpose Mixed-Signal Inference
    • Authors: Mohammad Bavandpour;Mohammad R. Mahmoodi;Dmitri B. Strukov;
      Pages: 98 - 106
      Abstract: We introduce “aCortex,” an extremely energy-efficient, fast, compact, and versatile neuromorphic processor architecture suitable for the acceleration of a wide range of neural network inference models. The most important feature of our processor is a configurable mixed-signal computing array of vector-by-matrix multiplier (VMM) blocks utilizing embedded nonvolatile memory arrays for storing weight matrices. Analog peripheral circuitry for data conversion and high-voltage programming are shared among a large array of VMM blocks to facilitate compact and energy-efficient analog-domain VMM operation of different types of neural network layers. Other unique features of aCortex include configurable chain of buffers and data buses, simple and efficient instruction set architecture and its corresponding multiagent controller, programmable quantization range, and a customized refresh-free embedded dynamic random access memory. The energy-optimal aCortex with 4-bit analog computing precision was designed in a 55-nm process with embedded NOR flash memory. Its physical performance was evaluated using experimental data from testing individual circuit elements and physical layout of key components for several common benchmarks, namely, Inception-v1 and ResNet-152, two state-of-the-art deep feedforward networks for image classification, and GNTM, Google’s deep recurrent network for language translation. The system-level simulation results for these benchmarks show the energy efficiency of 97, 106, and 336 TOp/J, respectively, combined with up to 15 TOp/s computing throughput and 0.27-MB/mm2 storage efficiency. Such estimated performance results compare favorably with those of previously reported mixed-signal accelerators based on much less mature aggressively scaled resistive switching memories.
      PubDate: June 2020
      Issue No: Vol. 6, No. 1 (2020)
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
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