Technology has become inevitable in human life, especially the growth of Internet of Things
(IoT), which enables communication and interaction with various devices. However, IoT has …

Neural networks (NNs) are effective machine learning models that require significant
hardware and energy consumption in their computing process. To implement NNs …

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 …

Stochastic computing (SC) allows for extremely low cost and low power implementations of
common arithmetic operations. However inherent random fluctuation error and long latency …

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

Stochastic computing (SC) has been used for low-cost and low power implementation of
neural networks. Inherent inaccuracy and long latency of processing random bit-streams …

Sorting is a common task in a wide range of applications from signal and image processing
to switching systems. For applications that require high performance, sorting is often …

The restricted Boltzmann machine (RBM) is a stochastic neural network capable of solving a
variety of difficult tasks including non-deterministic polynomial-time hard combinatorial …

Employing convolutional neural networks (CNNs) in embedded devices seeks novel low-
cost and energy efficient CNN accelerators. Stochastic computing (SC) is a promising low …

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 …