Over the past decade, deep-learning-based representations have demonstrated remarkable
performance in academia and industry. The learning capability of convolutional neural …

We review the problem of automating hardware-aware architectural design process of Deep
Neural Networks (DNNs). The field of Convolutional Neural Network (CNN) algorithm design …

Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning
(DL) is already present in many applications ranging from computer vision for medicine to …

Neural Architecture Search (NAS) methods have been growing in popularity. These
techniques have been fundamental to automate and speed up the time consuming and error …

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 …

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

The security and privacy concerns along with the amount of data that is required to be
processed on regular basis has pushed processing to the edge of the computing systems …

The hardware design space is high-dimensional and discrete. Systematic and efficient
exploration of this space has been a significant challenge. Central to this problem is the …

Automated neural architecture search (NAS) methods are now employed to routinely deliver
high-quality neural network architectures for various challenging data sets and reduce the …

While many companies are currently leveraging on Cloud, data centres and specialized
hardware (eg, GPUs and TPUs) to train very accurate Machine Learning models, the need to …