The exponential growth in computational power is slowing at a time when the amount of compute consumed by state-of-the-art deep learning (DL) workloads is rapidly growing. Model size, serving latency, and power constraints are a significant challenge in the deployment of DL models for many applications. Therefore, it is imperative to co-design algorithms, compilers, and hardware to accelerate advances in this field with holistic system-level and algorithm solutions that improve performance, power, and efficiency. This new book from Morgan & Claypool is your guide.
|Deep Learning , Compilers, and Processors for Large-Scale Production|
Andres Rodriguez, Intel
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 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.
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