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

The hardware implementation of deep neural networks (DNNs) has recently received
tremendous attention: many applications in fact require high-speed operations that suit a …

Deep neural network (DNN), as a very important machine learning technique in
classification and detection tasks for images, video, speech as wellas audio, has recently …

A serious challenge in artificial real-time applications is the hardware implementation of
deep neural networks (DNN). Among various methods, stochastic computing (SC)-based …

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 …

Artificial neural networks are powerful computational systems with interconnected neurons.
Generally, these networks have a very large number of computation nodes which forces the …

Along with the development of AI democratization, the machine learning approach, in
particular neural networks, has been applied to wide-range applications. In different …

Gradient descent (GD) is a widely used optimization algorithm in machine learning. In this
paper, a novel stochastic computing GD circuit (SC-GDC) is proposed by encoding the …

Neural networks are becoming prevalent in many areas, such as pattern recognition and
medical diagnosis. Stochastic computing is one potential solution for neural networks …

With increased interests of neural networks, hardware implementations of neural networks
have been investigated. Researchers pursue low hardware cost by using different …