Bayesian Inference on Binary Spiking Networks Leveraging Nanoscale Device Stochasticity

Prabodh Katti, Nicolas Skatchkovsky, Osvaldo Simeone, Bipin Rajendran, Bashir M. Al-Hashimi

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

Abstract

Bayesian Neural Networks (BNNs) can overcome the problem of overconfidence that plagues traditional frequentist deep neural networks, and are hence considered to be a key enabler for reliable AI systems. However, conventional hardware realizations of BNNs are resource intensive, requiring the imple-mentation of random number generators for synaptic sampling. Owing to their inherent stochasticity during programming and read operations, nanoscale memristive devices can be directly leveraged for sampling, without the need for additional hardware resources. In this paper, we introduce a novel Phase Change Memory (PCM)-based hardware implementation for BNNs with binary synapses. The proposed architecture consists of separate weight and noise planes, in which PCM cells are configured and operated to represent the nominal values of weights and to generate the required noise for sampling, respectively. Using experimentally observed PCM noise characteristics, for the ex-emplary Breast Cancer Dataset classification problem, we obtain hardware accuracy and expected calibration error matching that of an 8-bit fixed-point (FxP8) implementation, with projected savings of over 9× in terms of core area transistor count.

Original languageEnglish (US)
Title of host publicationISCAS 2023 - 56th IEEE International Symposium on Circuits and Systems, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665451093
DOIs
StatePublished - 2023
Event56th IEEE International Symposium on Circuits and Systems, ISCAS 2023 - Monterey, United States
Duration: May 21 2023May 25 2023

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume2023-May
ISSN (Print)0271-4310

Conference

Conference56th IEEE International Symposium on Circuits and Systems, ISCAS 2023
Country/TerritoryUnited States
CityMonterey
Period5/21/235/25/23

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Keywords

  • Bayesian inference
  • Phase Change Memory
  • Spiking Neural Networks
  • device noise
  • stochasticity

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