Probabilistic neural computing with advanced nanoscale MOSFETs

Authors:
Nor Hisham Hamid;Tong Boon Tang;Alan F. Murray
Affiliations:
Department of Electrical and Electronics Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 31750 Tronoh, Perak, Malaysia;School of Engineering, University of Edinburgh, King's Buildings, Mayfield Road, Edinburgh EH9 3JL, United Kingdom;School of Engineering, University of Edinburgh, King's Buildings, Mayfield Road, Edinburgh EH9 3JL, United Kingdom
Venue:
Neurocomputing
Year:
2011

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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.