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

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 …

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

Stochastic logic performs computation on data represented by random bit-streams. The
representation allows complex arithmetic to be performed with very simple logic, but it …

Stochastic computing (SC) is a low-cost design technique that has great promise in
applications such as image processing. SC enables arithmetic operations to be performed …

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 computing (SC), which is an approximate computation with probabilities, has
attracted attention as an alternative to deterministic computing. In this paper, we discuss a …

In this paper, we present an energy-efficient architecture of the Canny edge detector for
advanced mobile vision applications. Three key techniques for reducing computational …