Late Breaking Result: AQFP-aware Binary Neural Network Architecture Search
Authors: 
Zhengang Li, Xuan Shen, Geng Yuan, Masoud Zabihi, Tomoharu Yamauchi, Yanzhi Wang, Olivia ChenAuthors Info & Claims
Zhengang Li, Xuan Shen, Geng Yuan, Masoud Zabihi, Tomoharu Yamauchi, Yanzhi Wang, Olivia ChenAuthors Info & ClaimsArticle No.: 354, Pages 1 - 2
Abstract
Adiabatic Quantum-Flux-Parametron (AQFP) is a superconducting logic with extremely high energy efficiency. Recent research has made initial strides toward developing AQFP accelerator. However several critical challenges from both the hardware and software side remain, preventing the design from being a comprehensive solution. This paper proposes an AQFP-aware binary neural network architecture search framework that leverages software-hardware co-optimization to eventually search the AQFP-adapted neural network and the corresponding hardware configuration, providing a feasible AQFP-based solution for binary neural network (BNN) acceleration. Experimental results show that our framework consistently outperforms the representative AQFP-based framework.
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- Late Breaking Result: AQFP-aware Binary Neural Network Architecture Search
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Published: 07 November 2024
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