AntiDoteX: Attention-Based Dynamic Optimization for Neural Network Runtime Efficiency-Reference-Cited by-同舟云学术

AntiDoteX: Attention-Based Dynamic Optimization for Neural Network Runtime Efficiency

Published:2022-11 Issue:11 Volume:41 Page:4694-4707
ISSN:0278-0070
Container-title:IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
language:
Short-container-title:IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

Author:

Yu Fuxun¹^ORCID,Xu Zirui¹^ORCID,Liu Chenchen²^ORCID,Stamoulis Dimitrios³^ORCID,Wang Di³^ORCID,Wang Yanzhi⁴^ORCID,Chen Xiang¹^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA, USA

2. Department of Computer Science and Electrical Engineering, University of Maryland at Baltimore County, Baltimore, MD, USA

3. Microsoft Cognition, Microsoft, Redmond, WA, USA

4. Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA

Funder

NSF

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Software

Link

Reference44 articles.

3. Slimmable neural networks;yu;Proc 7th Int Conf Learn Represent (ICLR),2019

4. Once-for-all: Train one network and specialize it for efficient deployment;cai;Proc Int Conf Learn Represent (ICLR),2019

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献