Probabilistic neural computing with advanced nanoscale MOSFETs

Nor Hisham Hamid, Tong Boon Tang, Alan F. Murray

Research output: Contribution to journalArticlepeer-review

Abstract

The use of intrinsic nanoscale MOSFET noise for probabilistic computation is explored, using the continuous restricted Boltzmann machine (CRBM), a probabilistic neural model, as the exemplar architecture. The CRBM is modified by localising noise in its synaptic multipliers, exploiting random telegraph signal (RTS) noise in nanoscale MOSFETs. A look-up table (LUT) technique is adopted to link temporal noise data to the synaptic multipliers of a CRBM, trained to model simple, non-trivial data distributions. It is shown that, for such distributions at least, the CRBM with intrinsic nanoscale MOSFET noise can be trained to provide a useful model. (C) 2010 Elsevier B.V. All rights reserved.

Original languageEnglish
Pages (from-to)930-940
Number of pages11
JournalNeurocomputing
Volume74
Issue number6
DOIs
Publication statusPublished - 15 Feb 2011

Keywords

  • Probabilistic computing
  • Nanoscale MOSFET noise
  • Neuromorphic VLSI systems
  • LOW-FREQUENCY NOISE
  • RESTRICTED BOLTZMANN MACHINE
  • 1/F NOISE
  • NETWORKS
  • DEVICES
  • IMPACT
  • FLUCTUATIONS
  • ARCHITECTURE
  • ELECTRONICS
  • MODEL

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