The state-of-the-art approaches employ approximate computing to reduce the energy
consumption of DNN hardware. Approximate DNNs then require extensive retraining …

Current research in the area of Neural Networks (NN) has resulted in performance
advancements for a variety of complex problems. Especially, embedded system applications …

Leveraging the inherent error tolerance of a vast number of application domains that are
rapidly growing, approximate computing arises as a design alternative to improve the …

This article investigates an energy-efficient accuracy-configurable Dadda (X-Dadda)
multiplier. The structure employs the voltage overscaling and approximate width setting as …

Approximate computing (AxC) is increasingly emerging as a new design paradigm to
produce more efficient computation systems by judiciously reducing the computation quality …

As the artificial intelligence and Internet of Things (AIoT) develop rapidly, the deployment of
artificial neural networks in edge computing is becoming significant with great challenge …

Approximate computing trades off computation accuracy against energy efficiency. The
extent of approximation tolerance, however, significantly varies with a change in input …

Recently emerging applications, such as convolution neural networks (CNNs), which
process thousands of convolutional computations, require a large amount of power …

In recent years, many design automation methods have been developed to routinely create
approximate implementations of circuits and programs that show excellent trade-offs …

For many applications that exhibit strong error resilience, such as machine learning and
signal processing, energy efficiency and performance can be dramatically improved by …