Methodology for realizing VMM with binary RRAM arrays: Experimental demonstration of binarized-adaline using oxram crossbar

Sandeep Kaur Kingra, Vivek Parmar, Shubham Negi, Sufyan Khan, Boris Hudec, Tuo Hung Hou, Manan Suri*

*此作品的通信作者

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

In this paper, we present an efficient hardware mapping methodology for realizing vector matrix multiplication (VMM) on resistive memory (RRAM) arrays. Using the proposed VMM computation technique, we experimentally demonstrate a binarized-ADALINE (Adaptive Linear) classifier on an OxRAM crossbar. An 8×8 OxRAM crossbar with Ni/3-nm HfO2/7 nm Al-doped-TiO2/TiN device stack is used. Weight training for the binarized-ADALINE classifier is performed ex-situ on UCI cancer dataset. Post weight generation the OxRAM array is carefully programmed to binary weight-states using the proposed weight mapping technique on a custom-built testbench. Our VMM powered binarized-ADALINE network achieves a classification accuracy of 78% in simulation and 67% in experiments. Experimental accuracy was found to drop mainly due to crossbar inherent sneak-path issues and RRAM device programming variability.

原文English
主出版物標題2020 IEEE International Symposium on Circuits and Systems, ISCAS 2020 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781728133201
DOIs
出版狀態Published - 2020
事件52nd IEEE International Symposium on Circuits and Systems, ISCAS 2020 - Virtual, Online
持續時間: 10 10月 202021 10月 2020

出版系列

名字Proceedings - IEEE International Symposium on Circuits and Systems
2020-October
ISSN(列印)0271-4310

Conference

Conference52nd IEEE International Symposium on Circuits and Systems, ISCAS 2020
城市Virtual, Online
期間10/10/2021/10/20

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