Description
Morgan Deep Learning for Computer Architects by Brandon Reagen, Robert Adolf, Paul Whatmough
This is a primer written for computer architects in the new and rapidly evolving field of deep learning. It reviews how machine learning has evolved since its inception in the 1960s and tracks the key developments leading up to the emergence of the powerful deep learning techniques that emerged in the last decade._x000D__x000D_Machine learning, and specifically deep learning, has been hugely disruptive in many fields of computer science. The success of deep learning techniques in solving notoriously difficult classification and regression problems has resulted in their rapid adoption in solving real-world problems. The emergence of deep learning is widely attributed to a virtuous cycle whereby fundamental advancements in training deeper models were enabled by the availability of massive datasets and high-performance computer hardware._x000D__x000D_It also reviews representative workloads, including the most commonly used datasets and seminal networks across a variety of domains. In addition to discussing the workloads themselves, it also details the most popular deep learning tools and show how aspiring practitioners can use the tools with the workloads to characterize and optimize DNNs._x000D__x000D_The remainder of the book is dedicated to the design and optimization of hardware and architectures for machine learning. As high-performance hardware was so instrumental in the success of machine learning becoming a practical solution, this chapter recounts a variety of optimizations proposed recently to further improve future designs. Finally, it presents a review of recent research published in the area as well as a taxonomy to help readers understand how various contributions fall in context._x000D_ Table of contents :- _x000D_
Preface_x000D_
Introduction_x000D_
Foundations of Deep Learning_x000D_
Methods and Models_x000D_
Neural Network Accelerator Optimization: A Case Study_x000D_
A Literature Survey and Review_x000D_
Conclusion_x000D_
Bibliography_x000D_
Authors' Biographies_x000D_