We explore the modeling and design of efficient and flexible hardware accelerators.
* Indicates authors contributed equally to the work
An overview paper based on the tutorial "Efficient Processing of Deep Neural Networks: A Tutorial and Survey" is available here.
Our book based on the tutorial "Efficient Processing of Deep Neural Networks" is available here.
This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of the DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as a formalization and organization of key concepts from contemporary works that provides insights that may spark new ideas.
An excerpt of the book on "Key Metrics and Design Objectives" and "Advanced Technologies" available at here.