重新思考跨边缘与云平台的深度学习推理放置：多目标优化视角与未来方向
Rethinking Inference Placement for Deep Learning across Edge and Cloud Platforms: A Multi-Objective Optimization Perspective and Future Directions
AI总结：
本文从多目标优化视角综述了跨边缘与云平台的深度学习推理放置方法，权衡推理延迟、数据隐私和资源成本，并指出未来研究方向。
摘要：
随着物联网和移动设备的快速扩张，VR/AR和基于语言模型的聊天机器人等边缘智能应用已变得广泛普及。然而，资源受限的边缘设备往往无法承载日益庞大和复杂的深度学习（DL）模型。为缓解这些挑战，研究者提出了在用户设备、边缘服务器和云端之间优化并卸载深度学习模型分区的方法。在此设置下，用户可以利用不同服务来支持其智能应用。例如，边缘资源提供低响应延迟，而云平台则为计算密集型工作负载提供低货币成本的计算资源。然而，深度学习模型分区之间的通信可能引入传输瓶颈并带来数据泄露风险。近期研究旨在平衡准确性、计算延迟、传输延迟和隐私问题。他们通过模型压缩、模型蒸馏、传输压缩以及包括内部分类器在内的模型架构适配来解决这些问题。本综述通过研究最先进的模型卸载方法和模型适配技术对包含推理延迟、数据隐私和资源货币成本的多目标优化的影响，对其进行了情境化分析。
英文摘要：
Edge intelligent applications like VR/AR and language model based chatbots have become widespread with the rapid expansion of IoT and mobile devices. However, constrained edge devices often cannot serve the increasingly large and complex deep learning (DL) models. To mitigate these challenges, researchers have proposed optimizing and offloading partitions of DL models among user devices, edge servers, and the cloud. In this setting, users can take advantage of different services to support their intelligent applications. For example, edge resources offer low response latency. In contrast, cloud platforms provide low monetary cost computation resources for computation-intensive workloads. However, communication between DL model partitions can introduce transmission bottlenecks and pose risks of data leakage. Recent research aims to balance accuracy, computation delay, transmission delay, and privacy concerns. They address these issues with model compression, model distillation, transmission compression, and model architecture adaptations, including internal classifiers. This survey contextualizes the state-of-the-art model offloading methods and model adaptation techniques by studying their implication to a multi-objective optimization comprising inference latency, data privacy, and resource monetary cost.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
