EASE: Energy Optimization through Adaptation – A Review of Runtime Energy-Aware
Approximate Deep Learning Algorithms Page 1 P osted on 6 F eb 2024 — CC-BY 4.0 — h …

The escalating computational demands of deep learning and large-scale models have led to
a significant increase in energy consumption, highlighting the urgent need for more energy …

In this study, our primary aim is to develop a highly efficient multiplier architecture tailored for
approximate computing, with a specific focus on optimizing power efficiency. Multiplication …

We propose AMG, an open-source automated approximate multiplier generator for FPGAs
driven by Bayesian optimization (BO) with parallel evaluation. The proposed method …

Approximate circuits have become ubiquitous in error-resilient applications. Given their
widespread use, formal verification of these approximate designs is essential. Recently …

Approximate adders have received significant attention as they give benefits in power,
performance, and area for error-resilient applications. Due to their ubiquitous use, formal …

Approximate computing (AxC) has recently emerged as a successful approach for
optimizing energy consumption in error-tolerant applications, such as deep neural networks …

Designing high-performance integrated circuits that balance area, speed, and power is
increasingly challenging. This study optimises hardware implementation of FIR filters using …

Approximate multipliers offer an efficient approach to reduce power consumption in compute-
intensive applications, such as Deep Neural Networks (DNNs). However, current 8-bit …

As a potential alternative for implementing the large number of multiplications in
convolutional neural networks (CNNs), approximate multipliers (AMs) promise both high …