Can deep-sub-micron device noise be used as the basis for probabilistic neural computation?

This thesis explores the potential of probabilistic neural architectures for computation with future nanoscale Metal-Oxide-Semiconductor Field Effect Transistors (MOSFETs). In particular, the performance of a Continuous Restricted Boltzmann Machine {CRBM) implemented with generated noise of Random Telegraph Signal (RTS) and 1/ f form has been studied with reference to the 'typical' Gaussian implementation. In this study, a time domain RTS based noise analysis capability has been developed based upon future nanoscale MOSFETs, to represent the effect of nanoscale MOSFET noise on circuit implementation in particular the synaptic analogue multiplier which is subsequently used to implement stochastic behaviour of the CRBM. The result of this thesis indicates little degradation in performance from that of the typical Gaussian CRBM. Through simulation experiments, the CRBM with nanoscale MOSFET noise shows the ability to reconstruct training data, although it takes longer to converge to equilibrium. The results in this thesis do not prove that nanoscale MOSFET noise can be exploited in all contexts and with all data, for probabilistic computation. However, the result indicates, for the first time, that nanoscale MOSFET noise has the potential to be used for probabilistic neural computation hardware implementation. This thesis thus introduces a methodology for a form of technology-downstreaming and highlights the potential of probabilistic architecture for computation with future nanoscale MOSFETs