Modern computing schemes require large circuit areas and large energy consumption for
neuromorphic computing applications, such as recognition, classification, and prediction …

The common feature of nearly all logic and memory devices is that they make use of stable
units to represent 0's and 1's. A completely different paradigm is based on three-terminal …

Computing on time-based data is a recent evolution of research in stochastic computing. As
with stochastic computing, complex functions can be computed with remarkably low area …

Stochastic computing (SC) has emerged as a promising solution for performing complex
functions on large amounts of data to meet future computing demands. However, the …

Bayesian inference is an effective approach for solving statistical learning problems,
especially with uncertainty and incompleteness. However, Bayesian inference is a …

The emergence of XNOR networks seek to reduce the model size and computational cost of
neural networks for their deployment on specialized hardware requiring real-time processes …

We introducea spintronic analog to stochastic bit stream (SBS) converter (ASC) based on a
three terminal magnetic tunnel junction (MTJ) with a heavymetal layer. The critical current of …

This paper introduces a sudden power-outage resilient in-processor checkpointing for
energy-harvesting nonvolatile processors. In energy harvesting applications, a power supply …

This paper introduces area/energy-efficient gammatone filters based on stochastic
computation. The gammatone filter well expresses the performance of human auditory …

Growing uncertainty in design parameters (and therefore, in design functionality) renders
stochastic computing particularly promising, which represents and processes data as …