Resource-Constrained Neural Architecture Search on Edge Devices

The performance requirement of deep learning inevitably brings up with the expense of high computational complexity and memory requirements, to make it problematic for the deployment on resource-constrained devices. Edge computing, which distributedly organizes the computing node close to the data source and end-device, provides a feasible way to tackle the high-efficiency demand and substantial computational load. Whereas given edge device is resource-constrained and energy-sensitive, designing effective neural network architecture for specific edge device is urgent in the sense that deploys the deep learning application by the edge computing solution. Undoubtedly manually design the high-performing neural architectures is burdensome, let alone taking account of the resource-constraint for the specific platform. Fortunately, the success of Neural Architecture Search techniques come up with hope recently. This paper dedicates to directly employ multi-objective NAS on the resource-constrained edge devices. We first propose the framework of multi-objective NAS on edge device, which comprehensively considers the performance and real-world efficiency. Our improved MobileNet-V2 search space also strikes the scalability and practicality, so that a series of Pareto-optimal architectures are received. Benefits from the directness and specialization during search procedure, our experiment on JETSON NANO shows the comparable result with the state-of-the-art models on ImageNet.