EvoSh: Evolutionary Search with Shaving to Enable Power-Latency Tradeoff in Deep Learning Computing on Embedded Systems-Reference-Cited by-同舟云学术

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EvoSh: Evolutionary Search with Shaving to Enable Power-Latency Tradeoff in Deep Learning Computing on Embedded Systems

Published:2023-09-05 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 36th International System-on-Chip Conference (SOCC)
language:
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Author:

Kutukcu Basar¹,Baidya Sabur²,Raghunathan Anand³,Dey Sujit¹

Affiliation:

1. University of California,Department of Electrical and Computer Engineering,San Diego,CA,USA

2. University of Louisville,Department of Computer Science and Engineering,KY,USA

3. Purdue University,Department of Electrical and Computer Engineering,IN,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10256666/10256705/10257061.pdf?arnumber=10257061

Reference20 articles.

1. Practical Design Space Exploration

2. Data-driven offline optimization for architecting hardware accelerators;kumar;The Tenth International Conference on Learning Representations,2022

3. AxoNN

4. Active learning for multi-objective optimization;zuluaga;Proceedings of the ICML International Conference on Machine Learning,2013

5. Efficientnet: Rethinking model scaling for convolutional neural networks;tan;Proceedings of the 36th International Conference on Machine Learning ICML 2019,2019

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