Deep neural networks have proven to be particularly effective in visual and audio
recognition tasks. Existing models tend to be computationally expensive and memory …

In the past decade, Convolutional Neural Networks (CNNs) have demonstrated state-of-the-
art performance in various Artificial Intelligence tasks. To accelerate the experimentation and …

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

Interactive AI-powered services require low-latency evaluation of deep neural network
(DNN) models-aka"" real-time AI"". The growing demand for computationally expensive …

Memristor crossbars are circuits capable of performing analog matrix-vector multiplications,
overcoming the fundamental energy efficiency limitations of digital logic. They have been …

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 …

Convolutional neural networks (CNNs) are emerging as powerful tools for image
processing. Recent machine learning work has reduced CNNs' compute and data volumes …

Processing In-Memory (PIM) has shown a great potential to accelerate inference tasks of
Convolutional Neural Network (CNN). However, existing PIM architectures do not support …

Neural personalized recommendation is the cornerstone of a wide collection of cloud
services and products, constituting significant compute demand of cloud infrastructure. Thus …

Recent studies from several hyperscalars pinpoint to embedding layers as the most memory-
intensive deep learning (DL) algorithm being deployed in today's datacenters. This paper …