Reliable and robust RRAM-based neuromorphic computing

Grace Li Zhang, Bing Li, Ying Zhu, Shuhang Zhang, Tianchen Wang, Yiyu Shi, Tsung Yi Ho, Hai Li, Ulf Schlichtmann

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

RRAM-based crossbars are a promising hardware platform to accelerate computations in neural networks. Before such a crossbar can be used as an accelerator for neural networks, RRAM cells should be programmed to target resistances to represent weights in neural networks. However, this process degrades the valid range of the resistances of RRAM cells from the fresh state, called aging effect. Therefore, after a certain number of programming iterations, these RRAM cells cannot be programmed reliably anymore, affecting the classification accuracy of neural networks negatively. In addition, process variations during manufacturing and noise during programming of RRAM cells also lead to significant accuracy degradation. To solve the problems described above, in this paper, we introduce a software/hardware codesign framework to reduce the aging effect in RRAM crossbars. To counter process variations and noise, we first model them as random variables and then modify the computations in software training considering these variables. Simulation results show that the lifetime of RRAM crossbars can be extended by up to 11 times with the codesign framework and the mean value and the standard deviation of the inference accuracy under process variations and noise can be improved significantly.

Original languageEnglish
Title of host publicationGLSVLSI 2020 - Proceedings of the 2020 Great Lakes Symposium on VLSI
PublisherAssociation for Computing Machinery
Pages33-38
Number of pages6
ISBN (Electronic)9781450379441
DOIs
StatePublished - 7 Sep 2020
Event30th Great Lakes Symposium on VLSI, GLSVLSI 2020 - Virtual, Online, China
Duration: 7 Sep 20209 Sep 2020

Publication series

NameProceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI

Conference

Conference30th Great Lakes Symposium on VLSI, GLSVLSI 2020
Country/TerritoryChina
CityVirtual, Online
Period7/09/209/09/20

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