An 8T SRAM Based Digital Compute-In-Memory Macro For Multiply-And-Accumulate Accelerating

被引:6
作者
Wang, Zilin [1 ]
Luo, Hongyang [1 ]
Peng, ZeYang [1 ]
Chao, Xingchen [1 ]
He, Yajuan [1 ]
机构
[1] Univ Elect Sci & Technol China, Chengdu 610054, Peoples R China
来源
2023 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, ISCAS | 2023年
基金
中国国家自然科学基金;
关键词
Compute-in-memory; SRAM; Programmability; Digital approach;
D O I
10.1109/ISCAS46773.2023.10182037
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Compute-in-memory (CIM) has been a promising technology to reduce the data movement energy and latency, which is the bottleneck of Von Neumann architecture. Digital approaches in CIM macro have many advantages compared with analog counterparts, such as programmability and inference precision. However, previous works with digital approaches generally employ complex SRAM bit-cells and computational components, which cause a large area overhead. In this paper, we propose a new 8T SRAM bit-cell to reduce the overall area of the SRAM array, which is able to implement the 1-bit multiplication without read-disturb issue. Additionally, the interleaving adder tree and dual supply voltage strategy are employed for further reduction on area and power consumption of computational circuits. Besides, a result combination circuit is designed to increase the bit-precision flexibility. A 16Kb SRAM CIM macro with proposed techniques is designed in a 40-nm CMOS technology. The simulation results show that our work achieves 820 GOPS throughput and 94 TOPS/W energy efficiency with 4-b of both input and weight. It achieves 1.3x higher energy efficiency and 70% area reduction when compared to the recent state-of-the-art works.
引用
收藏
页数:5
相关论文
共 7 条
  • [1] An 89TOPS/W and 16.3TOPS/mm2 All-Digital SRAM-Based Full-Precision Compute-In Memory Macro in 22nm for Machine-Learning Edge Applications
    Chih, Yu-Der
    Lee, Po-Hao
    Fujiwara, Hidehiro
    Shih, Yi-Chun
    Lee, Chia-Fu
    Naous, Rawan
    Chen, Yu-Lin
    Lo, Chieh-Pu
    Lu, Cheng-Han
    Mori, Haruki
    Zhao, Wei-Cheng
    Sun, Dar
    Sinangil, Mahmut E.
    Chen, Yen-Huei
    Chou, Tan-Li
    Akarvardar, Kerem
    Liao, Hung-Jen
    Wang, Yih
    Chang, Meng-Fan
    Chang, Tsung-Yung Jonathan
    [J]. 2021 IEEE INTERNATIONAL SOLID-STATE CIRCUITS CONFERENCE (ISSCC), 2021, 64 : 252 - +
  • [2] Horowitz M, 2014, ISSCC DIG TECH PAP I, V57, P10, DOI 10.1109/ISSCC.2014.6757323
  • [3] A 1-16b Precision Reconfigurable Digital In-Memory Computing Macro Featuring Column-MAC Architecture and Bit-Serial Computation
    Kim, Hyunjoon
    Chen, Qian
    Yoo, Taegeun
    Kim, Tony Tae-Hyoung
    Kim, Bongjin
    [J]. IEEE 45TH EUROPEAN SOLID STATE CIRCUITS CONFERENCE (ESSCIRC 2019), 2019, : 345 - 348
  • [4] Deep Learning Algorithm for COVID-19 Classification Using Chest X-Ray Images
    Sharmila, V. J.
    Florinabel, Jemi D.
    [J]. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE, 2021, 2021
  • [5] A Dual-Split 6T SRAM-Based Computing-in-Memory Unit-Macro With Fully Parallel Product-Sum Operation for Binarized DNN Edge Processors
    Si, Xin
    Khwa, Win-San
    Chen, Jia-Jing
    Li, Jia-Fang
    Sun, Xiaoyu
    Liu, Rui
    Yu, Shimeng
    Yamauchi, Hiroyuki
    Li, Qiang
    Chang, Meng-Fan
    [J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS, 2019, 66 (11) : 4172 - 4185
  • [6] Wang JC, 2019, ISSCC DIG TECH PAP I, V62, P224, DOI 10.1109/ISSCC.2019.8662419
  • [7] A 55nm, 0.4V 5526-TOPS/W Compute-in-Memory Binarized CNN Accelerator for AIoT Applications
    Zhang, Hongtu
    Shu, Yuhao
    Jiang, Weixiong
    Yin, Zihan
    Zhao, Wenfeng
    Ha, Yajun
    [J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, 2021, 68 (05) : 1695 - 1699