An approach is described that investigates the potential of probabilistic "neural" architectures for computation with Deep Sub-Micrometer (DSM) MOSFETs. Initially, noisy MOSFET models are based upon those for a 0.35μm MOS technology with an exaggerated 1/f characteristic. We explore the manifestation of the 1/f characteristic at the output of 2-quadrant multiplier when the key n-channel MOSFETs are replaced by "noisy" MOSFETs. The stochastic behavior of this noisy multiplier has been mapped on to a software (Matlab) model of a Continuous Restricted Boltzmann Machine (CRBM) - an analogue-input stochastic computing structure. Simulation of this DSM CRBM implementation shows little degradation from that of a "perfect" CRBM. This paper thus introduces a methodology for a form of "technology-downstreaming" and highlights the potential of probabilistic architectures for DSM computation. © 2005 IEEE.