Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …

Deep Neural Networks (DNNs) are nowadays a common practice in most of the Artificial
Intelligence (AI) applications. Their ability to go beyond human precision has made these …

The growing energy and performance costs of deep learning have driven the community to
reduce the size of neural networks by selectively pruning components. Similarly to their …

The attention mechanism is becoming increasingly popular in Natural Language Processing
(NLP) applications, showing superior performance than convolutional and recurrent …

The advent of Deep Learning (DL) has radically transformed the computing industry across
the entire spectrum from algorithms to circuits. As myriad application domains embrace DL, it …

Modern computing systems are overwhelmingly designed to move data to computation. This
design choice goes directly against at least three key trends in computing that cause …

This book provides a structured treatment of the key principles and techniques for enabling
efficient processing of deep neural networks (DNNs). DNNs are currently widely used for …

Generalized Sparse Matrix-Matrix Multiplication (SpGEMM) is a ubiquitous task in various
engineering and scientific applications. However, inner product based SpGEMM introduces …

Graph convolutional neural networks (GCNs) have emerged as an effective approach to
extend deep learning for graph data analytics. Given that graphs are usually irregular, as …

Recent trends in deep learning (DL) imposed hardware accelerators as the most viable
solution for several classes of high-performance computing (HPC) applications such as …