Deep Learning Systems:Algorithms, Compilers, and Processors for Large-Scale Production @2020

2046

The purpose of this book is to provide a solid understanding of (1) the design, training, and applications of DL algorithms in 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 industry, explains the train- ing 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.

Unique in this book is the holistic exposition of the entire DL system stack, the emphasis on commercial applications, and the practical techniques to design models and accelerate their performance. The author is fortunate to work with hardware, software, data scientist, and re- search teams across many high-technology companies with hyperscale data centers. These com- panies employ many of the examples and methods provided throughout the book.

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