InstantNet: Automated Generation and Deployment of Instantaneously Switchable-Precision Networks
This addresses the need for efficient and adaptable DNN deployment on IoT devices, though it appears incremental as it builds on existing precision-switching methods.
The authors tackled the problem of enabling deep neural networks on IoT devices to adapt to time-varying resources by developing InstantNet, which automatically generates and deploys networks that can switch precision instantaneously, achieving consistent outperformance over state-of-the-art designs in experiments.
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.