NASH: Neural Architecture Search for Hardware-Optimized Machine Learning Models

arXiv:2403.01845v1 Announce Type: new
Abstract: As machine learning (ML) algorithms get deployed in an ever-increasing number of applications, these algorithms need to achieve better trade-offs between high accuracy, high throughput and low latency. This paper introduces NASH, a novel approach that applies neural architecture search to machine learning hardware. Using NASH, hardware designs can achieve not only high throughput and low latency but also superior accuracy performance. We present four versions of the NASH strategy in this paper, all of …

architecture arxiv cs.ai cs.cv cs.lg hardware machine machine learning machine learning models neural architecture search search type