Deep Neural Networks (DNNs) are very popular because of their high performance in
various cognitive tasks in Machine Learning (ML). Recent advancements in DNNs have …

With the increasing popularity of the Internet of Things and massive Machine Type
Communication technologies, the number of connected devices is rising. However, although …

Much has been discussed about artificial intelligence's negative environmental impacts due
to its power‐hungry Machine Learning algorithms and CO 2 CO _2 emissions linked to this …

The rapid proliferation of the Internet of Things and the dramatic resurgence of artificial
intelligence based application workloads have led to immense interest in performing …

The unprecedented progress in computational technologies led to a substantial proliferation
of artificial intelligence applications, notably in the era of big data and IoT devices. In the …

Edge training of deep neural networks (DNNs) is a desirable goal for continuous learning;
however, it is hindered by the enormous computational power required by training …

Deep neural networks (DNNs) are a type of artificial intelligence models that are inspired by
the structure and function of the human brain, designed to process and learn from large …

The globalization of the Integrated Circuit (IC) market is attracting an ever-growing number
of partners, while remarkably lengthening the supply chain. Thereby, security concerns …

This paper presents the Weight-aware Approximate Multiplication Architecture (W-AMA) for
Deep Neural Networks (DNNs). Considering the Gaussian-like weight distribution, it deploys …

Realizing a high-performance and energy-efficient circuit system is one of the critical tasks
for circuit designers. Conventional researchers always concentrated on the tradeoffs …