A Survey of Deep Learning on CPUs: Opportunities and Co-Optimizations

Mittal, S. and Rajput, P. and Subramoney, S. (2021) A Survey of Deep Learning on CPUs: Opportunities and Co-Optimizations. IEEE Trans. Neural Netw. Learning Syst.. pp. 1-21. ISSN 2162237X

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Abstract

PU is a powerful, pervasive, and indispensable platform for running deep learning (DL) workloads in systems ranging from mobile to extreme-end servers. In this article, we present a survey of techniques for optimizing DL applications on CPUs. We include the methods proposed for both inference and training and those offered in the context of mobile, desktop/server, and distributed systems. We identify the areas of strength and weaknesses of CPUs in the field of DL. This article will interest practitioners and researchers in the area of artificial intelligence, computer architecture, mobile systems, and parallel computing.

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IITH Creators:	IITH Creators ORCiD
Item Type:	Article
Uncontrolled Keywords:	Co-optimization, Distributed systems, End-servers, Mobile systems
Subjects:	Computer science
Divisions:	Department of Computer Science & Engineering
Depositing User:	Mrs Haseena VKKM
Date Deposited:	02 Nov 2021 11:44
Last Modified:	02 Nov 2021 11:44
URI:	http://raiithold.iith.ac.in/id/eprint/8887
Publisher URL:	https://ieeexplore.ieee.org/document/9410437/
OA policy:	https://v2.sherpa.ac.uk/id/publication/3532
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