一句话：提出基于模式的权重剪枝，在移动设备上实现高精度实时 DNN 推理。
移动设备上执行 DNN 推理面临高计算与存储需求，难以在保持高精度的同时满足实时性。现有权重剪枝方案处于两个极端：非结构化剪枝细粒度、精度高，但硬件不友好；结构化剪枝粗粒度、硬件高效，但精度损失较大。PatDNN 采用基于模式的权重剪枝，试图在精度与硬件效率之间取得折中，从而支持移动端实时 DNN 执行。标题表明其目标是在移动设备上实现实时推理，摘要未列出具体实验数值，评估主要关注移动端实时性、精度及剪枝的硬件友好性。
With the emergence of a spectrum of high-end mobile devices, many applications that formerly required desktop-level computation capability are being transferred to these devices. However, executing Deep Neural Networks (DNNs) inference is still challenging considering the high computation and storage demands, specifically, if real-time performance with high accuracy is needed. Weight pruning of DNNs is proposed, but existing schemes represent two extremes in the design space: non-structured pruning is fine-grained, accurate, but not hardware friendly; structured pruning is coarse-grained, hardware-efficient, but with higher accuracy loss.
@inproceedings{paperbot5475,
title = {PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning},
author = {Wei Niu and Xiaolong Ma and Sheng Lin and Shihao Wang and Xuehai Qian and Xue Lin and Yanzhi Wang and Bin Ren},
booktitle = {ASPLOS},
year = {2020},
doi = {10.1145/3373376.3378534}
}