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Stochastic computing (SC) is an unconventional method of computation that treats data as probabilities. Typically, each bit of an N-bit stochastic number (SN) Xis randomly chosen to be 1 with some probability p X , and X is generated and processed by conventional logic circuits. For instance, a single AND gate performs multiplication. The value X of an SN is measured by the density of 1 s in it, an information-coding scheme also found in biological neural systems. SC has uses in massively parallel systems and is very tolerant of soft errors. Its drawbacks include low accuracy, slow processing, and complex design needs. Its ability to efficiently perform tasks like communication decoding and neural network inference has rekindled interest in the field. Many challenges remain to be overcome, however, before SC becomes widespread. In this paper, we discuss the evolution of SC, mostly focusing on recent developments. We highlight the main challenges and discuss potential methods of overcoming them.
Alaghi et al. (2017) studied this question.