Signal Prediction for Digital Circuits by Sigmoidal Approximations Using Neural Networks

Abstract

Investigating the temporal behavior of digital circuits is a crucial step in system design, usually done via analog or digital simulation. Analog simulators like SPICE iteratively solve the differential equations characterizing the circuits' components numerically. Although unrivaled in accuracy, this is only feasible for small designs, due to the high computational effort even for short signal traces. Digital simulators use digital abstractions for predicting the timing behavior of a circuit. We advocate a novel approach, which generalizes digital traces to traces consisting of sigmoids, each parameterized by threshold crossing time and slope. For a given gate, we use an artificial neural network for implementing the transfer function that predicts, for any trace of input sigmoids, the parameters of the generated output sigmoids. By means of a prototype simulator, which can handle circuits consisting of inverters and NOR gates, we demonstrate that our approach operates substantially faster than an analog simulator, while offering a much better accuracy than a digital simulator.