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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication4th Electron Devices Technology and Manufacturing Conference, EDTM 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728125381
DOIs
Publication statusPublished - 2020 Apr
Event4th Electron Devices Technology and Manufacturing Conference, EDTM 2020 - Penang, Malaysia
Duration: 2020 Apr 62020 Apr 21

Publication series

Name4th Electron Devices Technology and Manufacturing Conference, EDTM 2020 - Proceedings

Conference

Conference4th Electron Devices Technology and Manufacturing Conference, EDTM 2020
CountryMalaysia
CityPenang
Period20-04-0620-04-21

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering
  • Electronic, Optical and Magnetic Materials

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