Approximate computing has recently emerged as a promising approach to energy-efficient
design of digital systems. Approximate computing relies on the ability of many systems and …

Stochastic computation has recently been proposed for implementing artificial neural
networks with reduced hardware and power consumption, but at a decreased accuracy and …

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 …

Invertible logic can operate in one of two modes: 1) a forward mode, in which inputs are
presented and a single, correct output is produced, and 2) a reverse mode, in which the …

Practically, any digital system includes sequential blocks represented using a model of finite
state machine (FSM). It is very important to improve such FSM characteristics as the number …

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

We propose a novel method for approximate hardware implementation of univariate math
functions with significantly fewer hardware resources compared to previous approaches …

This letter introduces a design and proof-of-concept implementation of Gabor filters based
on stochastic computation for area-efficient hardware. The Gabor filter exhibits a powerful …

Stochastic computing (SC) is an approximate computing technique that processes data in
the form of long pseudorandom bit-streams which can be interpreted as probabilities. Its key …

Methods for reducing power consumption in circuits of finite state machines (FSMs) are
discussed in this review. The review outlines the main approaches to solving this problem …