Models for characterizing noise based PCMOS circuits

Abstract

Quick and accurate error-rate prediction of Probabilistic CMOS (PCMOS) circuits is crucial for their systematic design and performance evaluation. While still in the early stage of research, PCMOS has shown potential to drastically reduce energy consumption at a cost of increased errors. Recently, a methodology has been proposed which could predict the error rates of cascade structures of blocks in PCMOS. This methodology requires error rates of unique blocks to predict the error rates of multiblock cascade structures composed of these unique blocks. In this article we present a new model for characterization of probabilistic circuits/blocks and present a procedure to find and characterize unique circuits/blocks. Unlike prior approaches, our new model distinguishes distinct filtering effects per output, thereby improving prediction accuracy by an average of 95% over the prior art by Palem and coauthors. Furthermore, we show two models where our new model with three stages is 18% more accurate, on average, than our simpler two-stage model. We apply our proposed models to Ripple Carry Adders and Wallace Tree Multipliers and show that using our models, the methodology of cascade structures can predict error rates of PCMOS circuits with reasonable accuracy (within 9%) in PCMOS for uniform voltages as well as multiple voltages. Finally, our approach takes seconds of simulation time whereas using HSPICE would take days of simulation time.