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 proliferation of connected devices including smartphones, novel network
connectivity and management methods are needed to meet user Quality of Experience …

At Facebook, machine learning provides a wide range of capabilities that drive many
aspects of user experience including ranking posts, content understanding, object detection …

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 …

Personalized recommendation systems leverage deep learning models and account for the
majority of data center AI cycles. Their performance is dominated by memory-bound sparse …

Deep neural networks have become the compelling solution for the applications such as
image classification, object detection, speech recognition, and machine translation …

Machine learning (ML) models are widely used in many important domains. For efficiently
processing these computational-and memory-intensive applications, tensors of these …

The co-existence of activation sparsity and model sparsity in convolutional neural network
(CNN) models makes sparsity-aware CNN hardware designs very attractive. The existing …

Point cloud analytics is poised to become a key workload on battery-powered embedded
and mobile platforms in a wide range of emerging application domains, such as …

In the era of artificial intelligence (AI), deep neural networks (DNNs) have emerged as the
most important and powerful AI technique. However, large DNN models are both storage …