Multi-Objective Surrogate-Model-Based Neural Architecture and Physical Design Co-Optimization of Energy Efficient Neural Network Hardware Accelerators-Reference-Cited by-同舟云学术

Multi-Objective Surrogate-Model-Based Neural Architecture and Physical Design Co-Optimization of Energy Efficient Neural Network Hardware Accelerators

Published:2023-01 Issue:1 Volume:70 Page:40-53
ISSN:1549-8328
Container-title:IEEE Transactions on Circuits and Systems I: Regular Papers
language:
Short-container-title:IEEE Trans. Circuits Syst. I

Author:

Wohrle Hendrik¹^ORCID,Schneider Felix²,Schlenke Fabian³,Lebold Denis³,De Lucas Alvarez Mariela⁴^ORCID,Kirchner Frank¹,Karagounis Michael²^ORCID

Affiliation:

1. Robotics Innovation Center, German Research Center for Artificial Intelligence (DFKI), Bremen, Germany

2. Department of Electrical Engineering, Dortmund University of Applied Sciences and Arts, Dortmund, Germany

3. Department of Information Technology, Institute for Communication Technology, Dortmund University of Applied Sciences and Arts, Dortmund, Germany

4. AG Robotik, University of Bremen, Bremen, Germany

Funder

Federal Ministry of Education and Research (BMBF) Germany in the KI-Sprung POMAA Project

Ministry of Culture and Science of the State of North Rhine-Westphalia (MKW NRW) in the Learning Chips Laboratory Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Hardware and Architecture

Link

http://xplorestaging.ieee.org/ielx7/8919/10025563/09999161.pdf?arnumber=9999161

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