新方法优化边缘设备上的深度神经网络，在精度损失极小的情况下降低延迟

报道来源 [3]

1. arXiv cs.LG TIER_1 English(EN) · Shuo Huai, Di Liu, Hao Kong, Xiangzhong Luo, Weichen Liu, Ravi Subramaniam, Christian Makaya, Qian Lin · 2026-07-10 04:00

   Collate: Collaborative Neural Network Learning for Latency-Critical Edge Systems

   arXiv:2607.08013v1 Announce Type: new Abstract: Federated Learning (FL) empowers multiple clients to collaboratively learn a model, enlarging the training data of each client for high accuracy while protecting data privacy. However, when deploying FL in real-time edge systems, th…

2. arXiv cs.LG TIER_1 English(EN) · Shuo Huai, Di Liu, Hao Kong, Weichen Liu, Ravi Subramaniam, Christian Makaya, Qian Lin · 2026-07-09 04:00

   Latency-Constrained DNN Architecture Learning for Edge Systems using Zerorized Batch Normalization

   arXiv:2607.06922v1 Announce Type: new Abstract: Deep learning applications have been widely adopted on edge devices, to mitigate the privacy and latency issues of accessing cloud servers. Deciding the number of neurons during the design of a deep neural network to maximize perfor…

3. arXiv cs.CV TIER_1 English(EN) · Qian Lin · 2026-07-08 02:35

   Latency-Constrained DNN Architecture Learning for Edge Systems using Zerorized Batch Normalization

   Deep learning applications have been widely adopted on edge devices, to mitigate the privacy and latency issues of accessing cloud servers. Deciding the number of neurons during the design of a deep neural network to maximize performance is not intuitive. Particularly, many appli…