For VPU, we achieve a 0.53% higher top-1 accuracy than Proxyless-mobile with a 1.49× speedup. Remarkably, HURRICANE achieves a 76.67% top-1 accuracy on ImageNet with a inference latency of only 16.5 ms for DSP, which is a 3.47% higher accuracy and a 6.35× inference speedup than FBNet-iPhoneX, respectively. Moreover, the discovered architectures achieve much lower latency and higher accuracy than current state-of-the-art efficient models. Extensive experiments on ImageNet demonstrate that our algorithm outperforms state-of-the-art hardware-aware NAS methods under the same latency constraint on three types of hardware. Unlike previous approaches that apply search algorithms on a small, human-designed search space without considering hardware diversity, we propose HURRICANE that explores the automatic hardware-aware search over a much larger search space and a two-stage search algorithm, to efficiently generate tailored models for different types of hardware. This paper addresses the hardware diversity challenge in Neural Architecture Search (NAS). Designing accurate and efficient convolutional neural architectures for vast amount of hardware is challenging because hardware designs are complex and diverse.
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