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 …

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

Real-time image-processing applications impose severe design constraints in terms of area
and power. Examples of interest include retinal implants for vision restoration and on-the-fly …

Maintaining the reliability of integrated circuits as transistor sizes continue to shrink to
nanoscale dimensions is a significant looming challenge for the industry. Computation on …

Stochastic computing (SC) is a re-emerging computing paradigm which enables ultra-low
power and massive parallelism in important applications like real-time image processing. It …

Deep convolutional neural networks (DCNNs) are one of the most promising deep learning
techniques and have been recognized as the dominant approach for almost all recognition …

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 unary computing provides low-area circuits. However, the required area
consuming stochastic number generators (SNGs) in these circuits can diminish their overall …

Arithmetic plays a major role in a computer? s performance and efficiency. Building new
computing platforms supported by the traditional binary arithmetic and silicon-based …