Deep neural networks (DNNs) have achieved great success in many applications. The
architectures of DNNs play a crucial role in their performance, which is usually manually …

The biological human brain model was used to inspire the idea of Artificial Neural Networks
(ANNs). The notion is then converted into a mathematical formulation and then into machine …

Deep neural networks (DNN) are now dominating in the most challenging applications of
machine learning. As DNNs can have complex architectures with millions of trainable …

Hardware-based neural networks are becoming attractive because of their superior
performance. One of the research challenges is to design such hardware using less area to …

Recent advances in algorithm-hardware co-design for deep neural networks (DNNs) have
demonstrated their potential in automatically designing neural architectures and hardware …

The architecture of deep neural networks is commonly determined via trial and error,
resulting in inefficiency and a lack of architecture interpretability. Recent research shows that …

This chapter gives an overview of evolutionary algorithm (EA) based methods applied to the
design of efficient implementations of deep neural networks (DNN). We introduce various …

The objective of the present study is to improve the genetic algorithm (GA) supremacy in
selecting the most suitable and relevant features within a highly dimensional dataset. This …

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

Evolvable hardware (EHW) is an emerging area of research that uses evolutionary
algorithms (EAs) to construct circuits without manual intervention. However, this technique …