Stochastic Data-driven Hardware Resilience to Efficiently Train Inference Models for Stochastic Hardware Implementations

Bonan Zhang, Lung Yen Chen, Naveen Verma

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

4 Scopus citations

Abstract

Machine-learning algorithms are being employed in an increasing range of applications, spanning high-performance and energy-constrained platforms. It has been noted that the statistical nature of the algorithms can open up new opportunities for throughput and energy efficiency, by moving hardware into design regimes not limited to deterministic models of computation. This work aims to enable high accuracy in machine-learning inference systems, where computations are substantially affected by hardware variability. Previous work has overcome this by training inference model parameters for a particular instance of variation-affected hardware. Here, training is instead performed for the distribution of variation-affected hardware, eliminating the need for instance-by-instance training. The approach is referred to as Stochastic Data-Driven Hardware Resilience (S-DDHR), and it is demonstrated for an in-memory-computing architecture based on magnetoresistive random-access memory (MRAM). S-DDHR successfully address different samples of stochastic hardware, which would otherwise suffer degraded performance due to hardware variability.

Original languageEnglish (US)
Title of host publication2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1388-1392
Number of pages5
ISBN (Electronic)9781479981311
DOIs
StatePublished - May 2019
Event44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom
Duration: May 12 2019May 17 2019

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2019-May
ISSN (Print)1520-6149

Conference

Conference44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
CountryUnited Kingdom
CityBrighton
Period5/12/195/17/19

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Keywords

  • Fault tolerance
  • In-memory Computing
  • Machine Learning
  • Statistical Computing

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