We extend, in two major ways, earlier work in which sigmoidal neural nonlinearities were implemented using stochastic counters. 1) We define the signal to noise limitations of unipolar and bipolar stochastic arithmetic and signal processing. 2) We generalize the use of stochastic counters to include neural transfer functions employed in Gaussian mixture models. The hardware advantages of (nonlinear) stochastic signal processing (SSP) may be offset by increased processing time; we quantify these issues. The ability to realize accurate Gaussian activation functions for neurons in pulsed digital networks using simple hardware with stochastic signals is also analyzed quantitatively.
Gaussian activation functions using Markov chains. Publishing Authors By Initials
