CREAM: Computing in ReRAM-Assisted Energy- and Area-Efficient SRAM for Reliable Neural Network Acceleration-Reference-Cited by-同舟云学术

CREAM: Computing in ReRAM-Assisted Energy- and Area-Efficient SRAM for Reliable Neural Network Acceleration

Published:2023-08 Issue:8 Volume:70 Page:3198-3211
ISSN:1549-8328
Container-title:IEEE Transactions on Circuits and Systems I: Regular Papers
language:
Short-container-title:IEEE Trans. Circuits Syst. I

Author:

Sun Yanan¹^ORCID,Wang Dengfeng¹,Xu Liukai¹,Chen Yiming²^ORCID,Li Zhi¹^ORCID,Liu Songyuan¹,He Weifeng¹^ORCID,Liu Yongpan²^ORCID,Yang Huazhong²^ORCID,Li Xueqing²^ORCID

Affiliation:

1. Department of Micro-Nano Electronics, Shanghai Jiao Tong University, Shanghai, China

2. Electronic Engineering Department, Tsinghua University, Beijing, BNRist, China

Funder

NSFC

National Key Research and Development Program of China

Natural Science Foundation of Shanghai

BirenTech Research

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Hardware and Architecture

Link

http://xplorestaging.ieee.org/ielx7/8919/10196078/10123351.pdf?arnumber=10123351

Reference48 articles.

1. A 28 nm 1 Mb time-domain computing-in-memory 6T-SRAM macro with a 6.6 ns latency, 1241GOPS and 37.01 TOPS/W for 8 b-MAC operations for edge-AI devices;wu;IEEE Int Solid-State Circuits Conf (ISSCC) Dig Tech Papers,2022

2. Q-PIM: A Genetic Algorithm based Flexible DNN Quantization Method and Application to Processing-In-Memory Platform

3. An 89 TOPS/W and 16.3 TOPS/mm2 all-digital SRAM-based full-precision compute-in memory macro in 22 nm for machine-learning edge applications;chih;IEEE Int Solid-State Circuits Conf (ISSCC) Dig Tech Papers,2021

4. BitBlade: Energy-Efficient Variable Bit-Precision Hardware Accelerator for Quantized Neural Networks

5. A 22nm 128-kb MRAM Row/Column-Parallel In-Memory Computing Macro with Memory-Resistance Boosting and Multi-Column ADC Readout