1. A Local Computing Cell and 6T SRAM-Based Computing-in-Memory Macro With 8-b MAC Operation for Edge AI Chips
- Author
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Yung-Ning Tu, William Shih, Jing-Hong Wang, Wei-Chiang Shih, Xin Si, Yajuan He, Yen-Chi Chou, Nan-Chun Lien, Yen-Lin Chung, Meng-Fan Chang, Qiang Li, Jian-Wei Su, Ta-Wei Liu, Ssu-Yen Wu, Pei-Jung Lu, Ren-Shuo Liu, Chih-Cheng Hsieh, Ruhui Liu, Chung-Chuan Lo, Kea-Tiong Tang, and Wei-Hsing Huang
- Subjects
Process variation ,business.industry ,Computer science ,Sense amplifier ,Header ,Static random-access memory ,Enhanced Data Rates for GSM Evolution ,Electrical and Electronic Engineering ,Macro ,business ,B-MAC ,Computer hardware ,Efficient energy use - Abstract
This article presents a computing-in-memory (CIM) structure aimed at improving the energy efficiency of edge devices running multi-bit multiply-and-accumulate (MAC) operations. The proposed scheme includes a 6T SRAM-based CIM (SRAM-CIM) macro capable of: 1) weight-bitwise MAC (WbwMAC) operations to expand the sensing margin and improve the readout accuracy for high-precision MAC operations; 2) a compact 6T local computing cell to perform multiplication with suppressed sensitivity to process variation; 3) an algorithm-adaptive low MAC-aware readout scheme to improve energy efficiency; 4) a bitline header selection scheme to enlarge signal margin; and 5) a small-offset margin-enhanced sense amplifier for robust read operations against process variation. A fabricated 28-nm 64-kb SRAM-CIM macro achieved access times of 4.1–8.4 ns with energy efficiency of 11.5–68.4 TOPS/W, while performing MAC operations with 4- or 8-b input and weight precision.
- Published
- 2021
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