Resource-Constrained Neural Architecture Search on Edge Devices
Bo Lyu, Hang Yuan, Longfei Lu, Yunye Zhang · IEEE Transactions on Network Science and Engineering · 2021
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.