Integration and Co-design of Memristive Devices and Algorithms for Artificial Intelligence

Wei Wang, Wenhao Song, Peng Yao, Yang Li, Joseph Van Nostrand, Qinru Qiu, Daniele Ielmini, J. Joshua Yang

Research output: Contribution to journalReview articlepeer-review

3 Scopus citations

Abstract

Memristive devices share remarkable similarities to biological synapses, dendrites, and neurons at both the physical mechanism level and unit functionality level, making the memristive approach to neuromorphic computing a promising technology for future artificial intelligence. However, these similarities do not directly transfer to the success of efficient computation without device and algorithm co-designs and optimizations. Contemporary deep learning algorithms demand the memristive artificial synapses to ideally possess analog weighting and linear weight-update behavior, requiring substantial device-level and circuit-level optimization. Such co-design and optimization have been the main focus of memristive neuromorphic engineering, which often abandons the “non-ideal” behaviors of memristive devices, although many of them resemble what have been observed in biological components. Novel brain-inspired algorithms are being proposed to utilize such behaviors as unique features to further enhance the efficiency and intelligence of neuromorphic computing, which calls for collaborations among electrical engineers, computing scientists, and neuroscientists.

Original languageEnglish (US)
Article number101809
JournaliScience
Volume23
Issue number12
DOIs
StatePublished - Dec 18 2020

Keywords

  • Computer Architecture
  • Hardware Co-design
  • Materials Science

ASJC Scopus subject areas

  • General

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