Presentation
Late Breaking Result: AQFP-aware Binary Neural Network Architecture Search
DescriptionAdiabatic Quantum-Flux-Parametron (AQFP) is a superconducting logic with extremely high energy efficiency. Recent research has made initial strides toward developing an AQFP-based crossbar accelerator. However several critical challenges from both the hardware and software side remain, preventing the design from being a comprehensive solution.
In this paper, we propose 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 can effectively search the AQFP-adapted neural network, consistently outperforming the representative AQFP-based framework.
In this paper, we propose 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 can effectively search the AQFP-adapted neural network, consistently outperforming the representative AQFP-based framework.
Event Type
Late Breaking Results Poster
TimeWednesday, June 266:00pm - 7:00pm PDT
LocationLevel 2 Lobby