Probabilistic neural computing with advanced nanoscale MOSFETs

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.