Bi-Directional and Operand-Controllable In-Memory Computing for Boolean Logic and Search Operations with Row and Column Directional SRAM (RC-SRAM)
-
Published:2024-08-22
Issue:8
Volume:15
Page:1056
-
ISSN:2072-666X
-
Container-title:Micromachines
-
language:en
-
Short-container-title:Micromachines
Author:
Xiao Han12ORCID, Zhao Ruiyong12, Liu Yulan12, Liu Yuanzhen12, Chen Jing1
Affiliation:
1. Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200031, China 2. University of Chinese Academy of Sciences, Beijing 100049, China
Abstract
The von Neumann architecture is no longer sufficient for handling large-scale data. In-memory computing has emerged as the potent method for breaking through the memory bottleneck. A new 10T SRAM bitcell with row and column control lines called RC-SRAM is proposed in this article. The architecture based on RC-SRAM can achieve bi-directional and operand-controllable logic-in-memory and search operations through different signal configurations, which can comprehensively respond to various occasions and needs. Moreover, we propose threshold-controlled logic gates for sensing, which effectively reduces the circuit area and improves accuracy. We validate the RC-SRAM with a 28 nm CMOS technology, and the results show that the circuits are not only full featured and flexible for customization but also have a significant increase in the working frequency. At VDD = 0.9 V and T = 25 °C, the bi-directional search frequency is up to 775 MHz and 567 MHz, and the speeds for row and column Boolean logic reach 759 MHz and 683 MHz.
Funder
Science and Technology Commission of Shanghai Municipality Zhangjiang National Laboratory the Department of Science and Technology of Guangdong Province
Reference19 articles.
1. Horowitz, M. (2014, January 9–13). 1.1 Computing’s energy problem (and what we can do about it). Proceedings of the 2014 IEEE International Solid-State Circuits Conference Digest of Technical Papers (ISSCC), San Francisco, CA, USA. 2. In-Memory Computing: Advances and Prospects;Verma;IEEE Solid-State Circuits Mag.,2019 3. Aga, S., Jeloka, S., Subramaniyan, A., Narayanasamy, S., Blaauw, D., and Das, R. (2017, January 4–8). Compute Caches. Proceedings of the 2017 IEEE International Symposium on High Performance Computer Architecture (HPCA), Austin, TX, USA. 4. Seshadri, V., Lee, D., Mullins, T., Hassan, H., Boroumand, A., Kim, J., Kozuch, M.A., Mutlu, O., Gibbons, P.B., and Mowry, T.C. (2017, January 14–17). Ambit: In-Memory Accelerator for Bulk Bitwise Operations Using Commodity DRAM Technology. Proceedings of the 2017 50th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), Boston, MA, USA. 5. He, M., Song, C., Kim, I., Jeong, C., Kim, S., Park, I., Thottethodi, M., and Vijaykumar, T.N. (2020, January 17–21). Newton: A DRAM-maker’s Accelerator-in-Memory (AiM) Architecture for Machine Learning. Proceedings of the 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), Athens, Greece.
|
|