| Summary: | Edge systems integrated with deep neural networks (DNNs) are deemed to pave the way for future artificial intelligence (AI). However, designing accurate and efficient DNNs for resource-limited edge systems is challenging as well as requires a huge amount of engineering efforts from human experts since the design space is highly complex and diverse. Also, previous works mostly focus on designing DNNs with less floating-point operations (FLOPs), but indirect FLOPs count does not necessarily reflect the complexity of DNNs. To tackle these, we, in this paper, propose a novel neural architecture search (NAS) approach, namely EdgeNAS, to automatically discover efficient DNNs for less capable edge systems. To this end, we propose an end-to-end learning-based latency estimator, which is able to directly approximate the architecture latency on edge systems while incurring negligible computational overheads. Further, we effectively incorporate the latency estimator into EdgeNAS with a uniform sampling strategy, which guides the architecture search towards an edge-efficient direction. Moreover, a search space regularization approach is introduced to balance the trade-off between efficiency and accuracy. We evaluate EdgeNAS on the edge platform, Nvidia Jetson Xavier, with three popular datasets. Experimental results demonstrate the superiority of EdgeNAS over state-of-the-art approaches in terms of latency, accuracy, number of parameters, and the search cost.
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