具有参数敏感性的重传机制用于高效的人工智能下载
Parametric-Sensitivity Aware Retransmission for Efficient AI Downloading
AI总结：
PASAR通过参数敏感性驱动的重传机制，提高AI模型下载效率并减少延迟。
摘要：
边缘人工智能（AI）应用在下一代移动网络中要求高效的AI模型下载技术以支持实时的设备端推理。然而，通过无线信道传输高维AI模型仍然具有挑战性，因为通信资源有限。为了解决这个问题，我们提出了一种参数敏感性意识的重传（PASAR）框架，根据参数对模型推理准确性的重要性，即参数敏感性，管理不同参数分组的无线资源使用。实证分析显示，敏感性分布高度右偏，表明只有少量参数显著影响模型性能。利用这一见解，我们设计了一种新的在线重传协议，即PASAR协议，该协议根据实时比特误码率（BER）测量和相关的参数敏感性适配性地终止数据包传输。该协议采用适应性的、按轮次停止准则，实现异构的、分组级重传，从而在保持整体模型功能的同时减少整体延迟。在各种深度神经网络架构和真实世界数据集上进行的广泛实验表明，PASAR在通信效率和延迟方面显著优于传统混合自动重传请求（HARQ）方案。
英文摘要：
The edge artificial intelligence (AI) applications in next-generation mobile networks demand efficient AI-model downloading techniques to support real-time, on-device inference. However, transmitting high-dimensional AI models over wireless channels remains challenging due to limited communication resources. To address this issue, we propose a parametric-sensitivity-aware retransmission (PASAR) framework that manages radio-resource usage of different parameter packets according to their importance on model inference accuracy, known as parametric sensitivity. Empirical analysis reveals a highly right-skewed sensitivity distribution, indicating that only a small fraction of parameters significantly affect model performance. Leveraging this insight, we design a novel online retransmission protocol, i.e., the PASAR protocol, that adaptively terminates packet transmission based on real-time bit error rate (BER) measurements and the associated parametric sensitivity. The protocol employs an adaptive, round-wise stopping criterion, enabling heterogeneous, packet-level retransmissions that preserve overall model functionality but reduce overall latency. Extensive experiments across diverse deep neural network architectures and real-world datasets demonstrate that PASAR substantially outperforms classical hybrid automatic repeat request (HARQ) schemes in terms of communication efficiency and latency.
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。
