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 …

Approximate computing has been considered to improve the accuracy-performance tradeoff
in error-tolerant applications. For many of these applications, multiplication is a key …

The topologically protected magnetic spin configurations known as Skyrmions offer
promising applications due to their stability, mobility, and localization. We emphasize how to …

Recent advances in neural networks (NNs) exhibit unprecedented success at transforming
large, unstructured data streams into compact higher-level semantic information for tasks …

Effectively tackling the upcoming “zettabytes” data explosion requires a huge quantum leap
in our computing power and energy efficiency. However, with the Moore's law dwindling …

Stochastic logic implementations of complex arithmetic functions, such as trigonometric,
exponential, and sigmoid, are derived based on truncated versions of their Maclaurin series …

Stochastic computing (SC) is a promising technique for applications that require low area
overhead and fault tolerance, but can tolerate relatively high latency. In the SC paradigm …

We give a short overview of stochastic computing (SC) and its uses. SC computes with
randomized bit-streams that loosely resemble the neural spike trains of the brain. Its key …

The rapid computerized simulation of stochastic computing (SC) systems is a challenging
problem. A method for agile simulation of SC image processing is proposed in this work. The …

This paper presents novel architectures for radial basis function (RBF) kernel computation
for support vector machine (SVM) classifier using stochastic logic. Stochastic computing …