Probabilistic neural computing with advanced nanoscale MOSFETs

Hamid, N.H. and Tang, T.B. and Murray, A.F. (2011) Probabilistic neural computing with advanced nanoscale MOSFETs. Neurocomputing, 74 (6). pp. 930-940. ISSN 09252312

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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. © 2010 Elsevier B.V.

Item Type: Article
Additional Information: cited By 2
Uncontrolled Keywords: Data distribution; Look up table; Nanoscale MOSFETs; Neural computing; Neural models; Neuromorphic VLSI systems; Non-trivial; Probabilistic computation; Probabilistic computing; Random telegraph signal noise; Restricted boltzmann machine; Temporal noise, MOSFET devices; Table lookup; Telegraph; Thermal noise, Nanostructured materials, metal oxide, article; artificial neural network; field effect transistor; learning algorithm; mathematical analysis; mathematical computing; priority journal; probability; signal noise ratio
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 09 Nov 2023 15:50
Last Modified: 09 Nov 2023 15:50
URI: https://khub.utp.edu.my/scholars/id/eprint/2240

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