On Memory System Design for Stochastic Computing

S. Karen Khatamifard, M. Hassan Najafi, Ali Ghoreyshi, Ulya Karpuzcu, David J Lilja

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Growing uncertainty in design parameters (and therefore, in design functionality) renders stochastic computing particularly promising, which represents and processes data as quantized probabilities. However, due to the difference in data representation, integrating conventional memory (designed and optimized for non-stochastic computing) in stochastic computing systems inevitably incurs a significant data conversion overhead. Barely any stochastic computing proposal to-date covers the memory impact. In this paper, as the first study of its kind to the best of our knowledge, we rethink the memory system design for stochastic computing. The result is a seamless stochastic system, StochMem, which features analog memory to trade the energy and area overhead of data conversion for computation accuracy. In this manner StochMem can reduce the energy (area) overhead by up-to 52.8% (93.7%) at the cost of at most 0.7% loss in computation accuracy.

Original languageEnglish (US)
Pages (from-to)117-121
Number of pages5
JournalIEEE Computer Architecture Letters
Volume17
Issue number2
DOIs
StatePublished - Jul 1 2018

Bibliographical note

Publisher Copyright:
© 2002-2011 IEEE.

Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.

Keywords

  • Stochastic computing
  • analog memory
  • energy-efficient design
  • memory system design
  • near-sensor processing

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