8T SRAM Cell as a Multibit Dot-Product Engine for Beyond Von Neumann Computing

被引:105
作者
Jaiswal, Akhilesh [1 ]
Chakraborty, Indranil [1 ]
Agrawal, Amogh [1 ]
Roy, Kaushik [2 ]
机构
[1] Purdue Univ, Sch Elect & Comp Engn, W Lafayette, IN 47907 USA
[2] Purdue Univ, Sch Elect Engn, W Lafayette, IN 47907 USA
基金
美国国家科学基金会;
关键词
Convolution; dot product; in-memory computing; SRAMs; von Neumann bottleneck;
D O I
10.1109/TVLSI.2019.2929245
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Large-scale digital computing almost exclusively relies on the von Neumann architecture, which comprises separate units for storage and computations. The energy-expensive transfer of data from the memory units to the computing cores results in the well-known von Neumann bottleneck. Various approaches aimed toward bypassing the von Neumann bottleneck are being extensively explored in the literature. These include in-memory computing based on CMOS and beyond CMOS technologies, wherein by making modifications to the memory array, vector computations can be carried out as close to the memory units as possible. Interestingly, in-memory techniques based on CMOS technology are of special importance due to the ubiquitous presence of field-effect transistors and the resultant ease of large-scale manufacturing and commercialization. On the other hand, perhaps the most important computation required for applications such as machine learning, etc., comprises the dot-product operation. Emerging nonvolatile memristive technologies have been shown to be very efficient in computing analog dot products in an in situ fashion. The memristive analog computation of the dot product results in much faster operation as opposed to digital vector in-memory bitwise Boolean computations. However, challenges with respect to large-scale manufacturing coupled with the limited endurance of memristors have hindered rapid commercialization of memristive-based computing solutions. In this paper, we show that the standard 8 transistor (8T) digital SRAM array can be configured as an analoglike in-memory multibit dot-product engine (DPE). By applying appropriate analog voltages to the read ports of the 8T SRAM array and sensing the output current, an approximate analog-digital DPE can be implemented. We present two different configurations for enabling multibit dot-product computations in the 8T SRAM cell array, without modifying the standard bit-cell structure. We also demonstrate the robustness of the present proposal in presence of nonidealities such as the effect of line resistances and transistor threshold voltage variations. Since our proposal preserves the standard 8T-SRAM array structure, it can be used as a storage element with standard read-write instructions and also as an on-demand analoglike dot-product accelerator.
引用
收藏
页码:2556 / 2567
页数:12
相关论文
共 35 条
  • [21] A 17.5-fJ/bit Energy-Efficient Analog SRAM for Mixed-Signal Processing
    Lee, Jinsu
    Shin, Dongjoo
    Kim, Youchang
    Yoo, Hoi-Jun
    [J]. IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS, 2017, 25 (10) : 2714 - 2723
  • [22] Analogue signal and image processing with large memristor crossbars
    Li, Can
    Hu, Miao
    Li, Yunning
    Jiang, Hao
    Ge, Ning
    Montgomery, Eric
    Zhang, Jiaming
    Song, Wenhao
    Davila, Noraica
    Graves, Catherine E.
    Li, Zhiyong
    Strachan, John Paul
    Lin, Peng
    Wang, Zhongrui
    Barnell, Mark
    Wu, Qing
    Williams, R. Stanley
    Yang, J. Joshua
    Xia, Qiangfei
    [J]. NATURE ELECTRONICS, 2018, 1 (01): : 52 - 59
  • [23] Liu CC, 2017, DES AUT CON, DOI [10.1145/3061639.3062310, 10.1109/ICCSN.2017.8230067]
  • [24] Liu R., 2018, P 55 ANN DES AUT C J, P21
  • [25] Muralimanohar N., 2009, HPL200985, P3
  • [26] Palm R. B., 2012, IMMMSC201231 U DENM, V5
  • [27] Posser G, 2014, IEEE I C ELECT CIRC, P682, DOI 10.1109/ICECS.2014.7050077
  • [28] Rumelhart D. E., 1985, ICS8506 CAL U
  • [29] ISAAC: A Convolutional Neural Network Accelerator with In-Situ Analog Arithmetic in Crossbars
    Shafiee, Ali
    Nag, Anirban
    Muralimanohar, Naveen
    Balasubramonian, Rajeev
    Strachan, John Paul
    Hu, Miao
    Williams, R. Stanley
    Srikumar, Vivek
    [J]. 2016 ACM/IEEE 43RD ANNUAL INTERNATIONAL SYMPOSIUM ON COMPUTER ARCHITECTURE (ISCA), 2016, : 14 - 26
  • [30] Si X, 2019, ISSCC DIG TECH PAP I, V62, P396, DOI 10.1109/ISSCC.2019.8662392