Paper Info
Title
1.1 The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design
Abstract
The past decade has seen a remarkable series of advances in machine learning, and in particular deeplearning approaches based on artificial neural networks, to improve our abilities to build more accurate systems across a broad range of areas, including computer vision, speech recognition, language translation, and natural language understanding tasks. This paper is a companion paper to a keynote talk at the 2020 International Solid-State Circuits Conference (ISSCC) discussing some of the advances in machine learning, and their implications on the kinds of computational devices we need to build, especially in the post-Moore's Lawera. It also discusses some of the ways that machine learning may be able to help with some aspects of the circuit design process. Finally, it provides a sketch of at least one interesting direction towards much larger-scale multi-task models that are sparsely activated and employ much more dynamic, exampleand task-based routing than the machine learning models of today.
Year
DOI
Venue
2020
10.1109/ISSCC19947.2020.9063049
2020 IEEE International Solid- State Circuits Conference - (ISSCC)
Keywords
DocType
ISSN
machine learning,task-based routing,circuit design process,natural language understanding tasks,language translation,speech recognition,computer vision,artificial neural networks,chip design,computer architecture,deep learning revolution
Conference
0193-6530
ISBN
Citations 
PageRank 
978-1-7281-3206-8
2
0.49
References 
Authors
22
1
Name
Order
Citations
PageRank
Jeffrey Dean111804457.69