Benchmarking the performance of heterogeneous stacked RRAM with CFETSRAM and MRAM for deep neural network application amidst variation and noise

Parthasarathi Pal, Sunanda Thunder, Min Jung Tsai, Po Tsang Huang, Yeong Her Wang

研究成果: Conference contribution

摘要

In this article we demonstrate and compare the performance of 32nm technology node compatible high-K and low-K stacked RRAM with CFET-SRAM and MRAM for binary deep neural network. We have fabricated heterogenous stacked RRAM with Sidoped Al2O3 and Ta2O5 as stacked layer for synaptic memory application. The device demonstrated an exorbitant on/off ratio ~ 4.2 x 103 with an ultra-low variation (σ ~ 6E-07 S). We have trained the neural network with 97.11% accuracy as baseline and observed the impact of conductance variation and read noise variation. We have also benchmarked the performance of our device with CFET-SRAM and MRAM technologies from other works and observed superior performance of our devices in terms of accuracy.

原文English
主出版物標題VLSI-TSA 2021 - 2021 International Symposium on VLSI Technology, Systems and Applications, Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781665419345
DOIs
出版狀態Published - 2021 四月 19
事件2021 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2021 - Hsinchu, Taiwan
持續時間: 2021 四月 192021 四月 22

出版系列

名字VLSI-TSA 2021 - 2021 International Symposium on VLSI Technology, Systems and Applications, Proceedings

Conference

Conference2021 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2021
國家/地區Taiwan
城市Hsinchu
期間21-04-1921-04-22

All Science Journal Classification (ASJC) codes

  • 硬體和架構
  • 電氣與電子工程
  • 控制和優化

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