An Area-Efficient In-Memory Implementation Method of Arbitrary Boolean Function Based on SRAM Array

被引:1
|
作者
Zhang, Sunrui [1 ]
Cui, Xiaole [1 ,3 ]
Wei, Feng [1 ]
Cui, Xiaoxin [2 ,3 ]
机构
[1] Peking Univ, Shenzhen Grad Sch, Shenzhen 518055, Peoples R China
[2] Peking Univ, Inst Microelect, Beijing 100871, Peoples R China
[3] Peng Cheng Lab, Shenzhen 518055, Peoples R China
关键词
Random access memory; Computer architecture; Table lookup; Merging; Parallel processing; SRAM cells; Logic functions; Arbitrary Boolean functions; in-memory computing; SRAM; synthesis method; NANOPARTICLES; OPERATIONS; MACRO;
D O I
10.1109/TC.2023.3301156
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
In-memory computing is an emerging computing paradigm to breakthrough the von-Neumann bottleneck. The SRAM based in-memory computing (SRAM-IMC) attracts great concerns from industries and academia, because the SRAM is technology compatible with the widely-used MOS devices. The digital SRAM-IMC scheme has advantages on stability and accuracy of computing results, compared with the analog SRAM-IMC schemes. However, few logic operations can be implemented by the current digital SRAM-IMC architectures. Designers have to insert some special logic modules to facilitate the complex computation. To address this issue, this work proposes an area-efficient implementation method of arbitrary Boolean function in SRAM array. Firstly, a two-input SRAM LUT is designed to realize the arbitrary two-input Boolean functions. Then, the logic merging and the spatial merging techniques are proposed to reduce the area consumption of the SRAM-IMC scheme. Finally, the SOP-based SRAM-IMC architecture is proposed, and the merged SOPs are mapped into and computed in it. The evaluation results on LGsynth'91, IWLS'93 and EPFL benchmarks show that, the area of the synthesis results based on the ABC tool is 3.69, 5.72 and 1.86 times of the circuit area from the proposed SRAM-IMC scheme in average respectively. Furthermore, the circuit area from the original SOP-based SRAM-IMC scheme is 2.07, 1.99 and 1.86 times in average of the circuit area from the proposed SRAM-IMC scheme respectively. The performance evaluation results show that the cycle consumption of the proposed SRAM-IMC scheme is independent to the scale of the input Boolean functions.
引用
收藏
页码:3416 / 3430
页数:15
相关论文
共 50 条
  • [31] An SRAM-Based Multibit In-Memory Matrix-Vector Multiplier With a Precision That Scales Linearly in Area, Time, and Power
    Khaddam-Aljameh, Riduan
    Francese, Pier-Andrea
    Benini, Luca
    Eleftheriou, Evangelos
    IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS, 2021, 29 (02) : 372 - 385
  • [32] Towards Generic Low-Power Area-Efficient Standard Cell Based Memory Architectures
    Meinerzhagen, P.
    Roth, C.
    Burg, A.
    53RD IEEE INTERNATIONAL MIDWEST SYMPOSIUM ON CIRCUITS AND SYSTEMS, 2010, : 129 - 132
  • [33] Energy-Efficient SNN Implementation Using RRAM-Based Computation In-Memory (CIM)
    El Arrassi, Asmae
    Gebregiorgis, Anteneh
    El Haddadi, Anass
    Hamdioui, Said
    PROCEEDINGS OF THE 2022 IFIP/IEEE 30TH INTERNATIONAL CONFERENCE ON VERY LARGE SCALE INTEGRATION (VLSI-SOC), 2022,
  • [34] R-inmac: 10T SRAM based reconfigurable and efficient in-memory advance computation for edge devices
    Narendra Singh Dhakad
    Eshika Chittora
    Vishal Sharma
    Santosh Kumar Vishvakarma
    Analog Integrated Circuits and Signal Processing, 2023, 116 : 161 - 184
  • [35] R-inmac: 10T SRAM based reconfigurable and efficient in-memory advance computation for edge devices
    Dhakad, Narendra Singh
    Chittora, Eshika
    Sharma, Vishal
    Vishvakarma, Santosh Kumar
    ANALOG INTEGRATED CIRCUITS AND SIGNAL PROCESSING, 2023, 116 (03) : 161 - 184
  • [36] Efficient Implementation of Multiplexer and Full-Adder Functions Based on Memristor Arrays for In-memory Computing
    Gan, Zhouchao
    Zhang, Chenyu
    Ma, Yinghao
    Zhang, Dongdong
    Miao, Xiangshui
    Wang, Xingsheng
    8TH IEEE ELECTRON DEVICES TECHNOLOGY & MANUFACTURING CONFERENCE, EDTM 2024, 2024, : 136 - 138
  • [37] Reconfigurable and Efficient Implementation of 16 Boolean Logics and Full-Adder Functions with Memristor Crossbar for Beyond von Neumann In-Memory Computing
    Song, Yujie
    Wang, Xingsheng
    Wu, Qiwen
    Yang, Fan
    Wang, Chengxu
    Wang, Meiqing
    Miao, Xiangshui
    ADVANCED SCIENCE, 2022, 9 (15)
  • [38] Area-efficient Transposable Crossbar Synapse Memory Using 6T SRAM Bit Cell for Fast Online Learning of Neuromorphic Processors
    Koo, Jongeun
    Kim, Jinseok
    Ryu, Sungju
    Kim, Chulsoo
    Kim, Jae-Joon
    JOURNAL OF SEMICONDUCTOR TECHNOLOGY AND SCIENCE, 2020, 20 (02) : 195 - 203
  • [39] High-Throughput, Area-Efficient, and Variation-Tolerant 3-D In-Memory Compute System for Deep Convolutional Neural Networks
    Veluri, Hasita
    Li, Yida
    Niu, Jessie Xuhua
    Zamburg, Evgeny
    Thean, Aaron Voon-Yew
    IEEE INTERNET OF THINGS JOURNAL, 2021, 8 (11) : 9219 - 9232
  • [40] Architecture design of area-efficient SRAM-based multi-symbol arithmetic encoder in H.264/AVC
    Chen, Yu-Jen
    Tsai, Chen-Han
    Chen, Liang-Gee
    2006 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, VOLS 1-11, PROCEEDINGS, 2006, : 2621 - +