The limited and dynamically varied resources on edge devices motivate us to deploy an optimized deep neural network that can adapt its sub-networks to fit in different resource constraints. However, existing works often build sub-networks through searching different network architectures in a hand-crafted sampling space, which not only can result in a subpar performance but also may cause on-device re-configuration overhead. In this paper, we propose a novel training algorithm, Dynamic REal-time Sparse Subnets (DRESS). DRESS samples multiple sub-networks from the same backbone network through row-based unstructured sparsity, and jointly trains these sub-networks in parallel with weighted loss. DRESS also exploits strategies including parameter reusing and row-based fine-grained sampling for efficient storage consumption and efficient on-device adaptation. Extensive experiments on public vision datasets show that DRESS yields significantly higher accuracy than state-of-the-art sub-networks.

本文提出了一种名为 DRESS 的新型训练算法，用于从同一主干网络中通过基于行的非结构稀疏性采样多个子网络，并通过加权损失并行训练这些子网络，以及利用参数重用和基于行的精细采样策略来实现高效存储和设备适应。在公共视觉数据集上的大量实验证明，DRESS比最先进的子网络具有显着更高的准确性。

DRESS：动态实时稀疏子网络