Rodriguez Andres. Deep Learning Systems: Algorithms, Compilers, and Processors for Large-scale Production

Morgan & Claypool, 2020. — 236 p. — (Synthesis Lectures on Computer Architecture). — ISBN10: 1681739666, 13 978-1681739663.This book describes deep learning systems: the algorithms, compilers, and processor components to efficiently train and deploy deep learning models for commercial applications.The purpose of this book is to provide a solid understanding of (1) the design, training, and applications of DL algorithms in the industry; (2) the compiler techniques to map deep learning code to hardware targets; and (3) the critical hardware features that accelerate DL systems. This book aims to facilitate co-innovation for the advancement of DL systems. It is written for engineers working in one or more of these areas who seek to understand the entire system stack in order to better collaborate with engineers working in other parts of the system stack.
The book details advancements and adoption of DL models in the industry, explains the training and deployment process, describes the essential hardware architectural features needed for today's and future models, and details advances in DL compilers to efficiently execute algorithms across various hardware targets.