通过分割学习降低视觉分割推理延迟，提升边缘设备实时性能。
Split Learning in Computer Vision for Semantic Segmentation Delay Minimization
- 将神经网络分拆至边缘与服务器，减少传输数据量
- 联合优化带宽、切分层和资源分配，延迟显著下降
- 适合自动驾驶等资源受限的实时视觉场景
本文提出一种新型分割学习（Split Learning, SL）方法，旨在最小化语义分割任务中的推理延迟，满足资源受限设备在实时计算机视觉应用中的需求。语义分割对自动驾驶和智慧城市基础设施至关重要，但因高计算与通信负载导致显著延迟。传统集中式处理效率低下，难以满足实时性要求。SL通过将深度神经网络（DNN）分置于边缘设备与中心服务器，实现本地数据处理并减少传输数据量。本文贡献在于联合优化带宽分配、边缘端DNN的切分层选择以及中心服务器的处理资源分配。研究了并行与串行数据处理两种场景，提出了低复杂度启发式方案，在保持近似最优性能的同时降低计算开销。数值结果表明，该方法有效降低了推理延迟，展示了SL在动态、资源受限环境下的实时视觉应用潜力。
原文摘要 · Abstract (English)
In this paper, we propose a novel approach to minimize the inference delay in semantic segmentation using split learning (SL), tailored to the needs of real-time computer vision (CV) applications for resource-constrained devices. Semantic segmentation is essential for applications such as autonomous vehicles and smart city infrastructure, but faces significant latency challenges due to high computational and communication loads. Traditional centralized processing methods are inefficient for such scenarios, often resulting in unacceptable inference delays. SL offers a promising alternative by partitioning deep neural networks (DNNs) between edge devices and a central server, enabling localized data processing and reducing the amount of data required for transmission. Our contribution includes the joint optimization of bandwidth allocation, cut layer selection of the edge devices' DNN, and the central server's processing resource allocation. We investigate both parallel and serial data processing scenarios and propose low-complexity heuristic solutions that maintain near-optimal performance while reducing computational requirements. Numerical results show that our approach effectively reduces inference delay, demonstrating the potential of SL for improving real-time CV applications in dynamic, resource-constrained environments.
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