Write–Verify-Free MLC RRAM Using Nonbinary Encoding for AI Weight Storage at the Edge-Reference-Cited by-同舟云学术

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Write–Verify-Free MLC RRAM Using Nonbinary Encoding for AI Weight Storage at the Edge

Published:2024-02 Issue:2 Volume:32 Page:283-290
ISSN:1063-8210
Container-title:IEEE Transactions on Very Large Scale Integration (VLSI) Systems
language:
Short-container-title:IEEE Trans. VLSI Syst.

Author:

An Junjie¹^ORCID,Zhou Zhidao²,Wang Linfang²^ORCID,Ye Wang²^ORCID,Li Weizeng²,Gao Hanghang²,Li Zhi²,Tian Jinghui³,Wang Yan²^ORCID,Hu Hongyang²^ORCID,Yue Jinshan²^ORCID,Fan Lingyan⁴^ORCID,Long Shibing¹^ORCID,Liu Qi³^ORCID,Dou Chunmeng²^ORCID

Affiliation:

1. School of Microelectronics, University of Science and Technology of China, Hefei, China

2. Institute of Microelectronics, Chinese Academy of Sciences, Beijing, China

3. Frontier Institute of Chip and System, State Key Laboratory of ASIC and System, Fudan University, Shanghai, China

4. Department of Communication Engineering, Hangzhou Dianzi University, Hangzhou, China

Funder

National Key Research and Development Program

NSFC

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Hardware and Architecture,Software

Link

http://xplorestaging.ieee.org/ielx7/92/10411971/10275806.pdf?arnumber=10275806

Reference35 articles.

1. Deep learning

2. Scaling for edge inference of deep neural networks

3. TinyML: Current Progress, Research Challenges, and Future Roadmap

4. Innovation at the edge: IoT 2.0

5. Resistive random access memory (RRAM) technology: From material, device, selector, 3D integration to bottom-up fabrication

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