TL;DR我们提出了一种基于一次训练(OFA)网络的方法,通过将训练和搜索分离,支持多种网络结构并可以快速选择以减少计算成本。使用渐进式缩减算法进行高效训练,在各种边缘设备上具有优异的表现和更少的能源成本,赢得了Low Power Computer Vision Challenge的胜利。
Abstract
Efficient deployment of deep learning models requires specialized neural network architectures to best fit different hardware platforms and efficiency constraints (defined as deployment scenarios). Traditional approaches either manually design or use AutoML to search a specialized