When a computational task tolerates a relaxation of its specification or when an algorithm
tolerates the effects of noise in its execution, hardware, system software, and programming …

As one of the most promising energy-efficient computing paradigms, approximate computing
has gained a lot of research attention in the past few years. This paper presents a survey of …

This article presents a survey of the different modern high-level synthesis (HLS) design
space exploration (DSE) techniques that have been proposed so far to automatically …

Approximate computing is an emerging paradigm that, by relaxing the requirement for full
accuracy, offers benefits in terms of design area and power consumption. This paradigm is …

Using machine learning to solve combinatorial optimization (CO) problems is challenging,
especially when the data is unlabeled. This work proposes an unsupervised learning …

Approximate computing forms a design alternative that exploits the intrinsic error resilience
of various applications and produces energy-efficient circuits with small accuracy loss. In this …

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

Approximate computing is an emerging paradigm for develo** highly energy-efficient
computing systems such as various accelerators. In the literature, many libraries of …

In recent years, approximate computing has emerged as a promising approach to trade off
quality of computed outputs for energy savings. In this paper, we present an approximate …

Approximate computing is an emerging paradigm where design accuracy can be traded off
for benefits in design metrics such as design area, power consumption or circuit complexity …