InstantNet: Automated Generation and Deployment of Instantaneously Switchable-Precision Networks
Yonggan Fu
Zhongzhi Yu
Yongan Zhang
Yifan Jiang
Chaojian Li
Yongyuan Liang
Mingchao Jiang
Zhangyang Wang
Yingyan Lin

Abstract
The promise of Deep Neural Network (DNN) powered Internet of Thing (IoT) devices has motivated a tremendous demand for automated solutions to enable fast development and deployment of efficient (1) DNNs equipped with instantaneous accuracy-efficiency trade-off capability to accommodate the time-varying resources at IoT devices and (2) dataflows to optimize DNNs' execution efficiency on different devices. Therefore, we propose InstantNet to automatically generate and deploy instantaneously switchable-precision networks which operate at variable bit-widths. Extensive experiments show that the proposed InstantNet consistently outperforms state-of-the-art designs.
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