Neurophysics-inspired parallel architecture with resistive crosspoint array for dictionary learning

Deepak Kadetotad, Zihan Xu, Abinash Mohanty, Pai Yu Chen, Binbin Lin, Jieping Ye, Sarma Vrudhula, Shimeng Yu, Yu Cao, Jae-sun Seo

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

10 Scopus citations

Abstract

This paper proposes a parallel architecture with resistive crosspoint array. The design of its two essential operations, Read and Write, is inspired by the biophysical behavior of a neural system, such as integrate-and-fire and time-dependent synaptic plasticity. The proposed hardware consists of an array with resistive random access memory (RRAM) and CMOS peripheral circuits, which perform matrix product and dictionary update in a fully parallel fashion, at the speed that is independent of the matrix dimension. The entire system is implemented in 65nm CMOS technology with RRAM to realize high-speed unsupervised dictionary learning. As compared to state-of-the-art software approach, it achieves more than 3000X speedup, enabling real-time feature extraction on a single chip.

Original languageEnglish (US)
Title of host publicationIEEE 2014 Biomedical Circuits and Systems Conference, BioCAS 2014 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages536-539
Number of pages4
ISBN (Electronic)9781479923465
DOIs
StatePublished - Dec 9 2014
Event10th IEEE Biomedical Circuits and Systems Conference, BioCAS 2014 - Lausanne, Switzerland
Duration: Oct 22 2014Oct 24 2014

Publication series

NameIEEE 2014 Biomedical Circuits and Systems Conference, BioCAS 2014 - Proceedings

Other

Other10th IEEE Biomedical Circuits and Systems Conference, BioCAS 2014
Country/TerritorySwitzerland
CityLausanne
Period10/22/1410/24/14

ASJC Scopus subject areas

  • Hardware and Architecture
  • Biomedical Engineering

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