Steerable Zero-Shot Neural Architecture Search for Efficient Edge Inference

Recent advancements in Neural Architecture Search (NAS) have introduced methods capable of identifying optimal neural network architectures in minutes on Graphical Processing Units (GPUs) using zero-shot proxies, but mainly focus on single-objective optimization. NAS is also used to discover efficient architectures for edge devices. However, addressing the diverse hardware and application constraints specific to edge platforms remains a significant challenge. In this paper, we introduce a zero-shot NAS approach designed to generate hardware-aware architectures, combined with a selection technique that allows adaptable model optimization across various deployment scenarios without re-executing the search. We demonstrate the flexibility of our solution by benchmarking the Google Coral Edge Tensor Processing Unit (TPU). Our technique led to the efficient exploration of the architecture space of NAS-Bench-201 (NB201) in under a minute, accelerating the search by 25× compared to previous work while maintaining comparable accuracies of 93.24% on CIFAR-10 and 42.99% on ImageNet16-120.