Edge computing and artificial intelligence (AI), especially deep learning algorithms, are
gradually intersecting to build the novel system, namely edge intelligence. However, the …

Abstract Knowledge extraction through machine learning techniques has been successfully
applied in a large number of application domains. However, apart from the required …

Deep neural networks (DNNs) have recently achieved impressive success across a wide
range of real-world vision and language processing tasks, spanning from image …

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 …

Abstract Neural Architecture Search (NAS) has shown its potential in aiding in the
development of more efficient neural networks. In regard to hardware, efficiency often …

HMPSoCs combine different processors on a single chip. They enable powerful embedded
devices, which increasingly perform ML inference tasks at the edge. State-of-the-art …

Differentiable neural architecture search (NAS) methods have achieved comparable results
for low search costs and high performance. Existing differentiable methods focus on …

With the increasing availability of large-scale datasets and powerful computing paradigms,
convolutional neural networks (CNNs) have empowered a wide range of intelligent …

Deep neural networks (DNNs) have achieved remarkable results and have become the
mainstay of many applications including autonomous driving and emerging AI-enabled …

Neural networks are extensively utilized for building performance prediction models for high-
performance computing systems. It is challenging to construct the neural network …