Ultralow Power Neuromorphic Accelerator for Deep Learning Using Ni/HfO2/TiN Resistive Random Access Memory

Hoang Hiep Le, Wei Chen Hong, Jian Wei Du, Tsung Han Lin, Yi Xiu Hong, I. Hsuan Chen, Wen Jay Lee, Nan Yow Chen, Darsen D. Lu

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

In this article we explore Ni/HfO2/TiN resistive random access memory (RRAM) as ultralow power synaptic element in deep neural networks (DNN) for artificial intelligence applications. Low power RRAM devices are fabricated and measured, with very low RESET current and 2-3 orders of resistance window. The SET-RESET current-voltage characteristics, high-and low-resistance state statistical distribution, and analog programming characteristics are calibrated to analytical models. Training and inference for the MNIST handwritten digits dataset using a multilayer perceptron was simulated based on the calibrated model using CIMulator, a novel neuromorphic simulation platform for compute-in-memory circuitry to predict DNN inference accuracy and energy consumption. Despite larger inherent device-to-device variability due to low on current, 97% inference accuracy is achieved with only 2-bit for the weights using weight update accumulation technique.

原文English
主出版物標題4th Electron Devices Technology and Manufacturing Conference, EDTM 2020 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781728125381
DOIs
出版狀態Published - 2020 四月
事件4th Electron Devices Technology and Manufacturing Conference, EDTM 2020 - Penang, Malaysia
持續時間: 2020 四月 62020 四月 21

出版系列

名字4th Electron Devices Technology and Manufacturing Conference, EDTM 2020 - Proceedings

Conference

Conference4th Electron Devices Technology and Manufacturing Conference, EDTM 2020
國家/地區Malaysia
城市Penang
期間20-04-0620-04-21

All Science Journal Classification (ASJC) codes

  • 硬體和架構
  • 電氣與電子工程
  • 工業與製造工程
  • 電子、光磁材料

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