Computational memory-based inference and training of deep neural networks

A. Sebastian, I. Boybat, M. Dazzi, I. Giannopoulos, V. Jonnalagadda, V. Joshi, G. Karunaratne, B. Kersting, R. Khaddam-Aljameh, S. R. Nandakumar, A. Petropoulos, C. Piveteau, T. Antonakopoulos, B. Rajendran, M. Le Gallo, E. Eleftheriou

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

2 Scopus citations

Abstract

In-memory computing is an emerging computing paradigm where certain computational tasks are performed in place in a computational memory unit by exploiting the physical attributes of the memory devices. Here, we present an overview of the application of in-memory computing in deep learning, a branch of machine learning that has significantly contributed to the recent explosive growth in artificial intelligence. The methodology for both inference and training of deep neural networks is presented along with experimental results using phase-change memory (PCM) devices.

Original languageEnglish (US)
Title of host publication2019 Symposium on VLSI Circuits, VLSI Circuits 2019 - Digest of Technical Papers
PublisherInstitute of Electrical and Electronics Engineers Inc.
PagesT168-T169
ISBN (Electronic)9784863487185
DOIs
StatePublished - Jun 2019
Event33rd Symposium on VLSI Circuits, VLSI Circuits 2019 - Kyoto, Japan
Duration: Jun 9 2019Jun 14 2019

Publication series

NameIEEE Symposium on VLSI Circuits, Digest of Technical Papers
Volume2019-June

Conference

Conference33rd Symposium on VLSI Circuits, VLSI Circuits 2019
CountryJapan
CityKyoto
Period6/9/196/14/19

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering

Keywords

  • In-memory computing
  • PCM
  • deep learning

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