基于有向信息瓶颈的神经形态无线设备-边缘协同推理
Neuromorphic Wireless Device-Edge Co-Inference via the Directed Information Bottleneck
AI总结：
本文提出神经形态无线设备-边缘协同推理系统，设备端用神经形态硬件高效完成感知、处理与通信，并通过有向信息瓶颈准则减少通信开销，同时保留端到端语义任务所需的关键信息，实验验证了该架构的有效性。
摘要：
下一代无线系统的一个重要用例是设备-边缘协同推理，其中语义任务在设备与边缘服务器之间进行划分。设备负责数据采集和数据的部分处理，而远程服务器则根据从设备接收到的信息完成给定任务。通常要求设备端的处理和通信尽可能高效地运行，而边缘端则拥有更多的计算资源。为了应对这类场景，我们引入一种新的系统解决方案，称为神经形态无线设备-边缘协同推理。根据该方案，设备使用神经形态硬件运行感知、处理和通信单元，而服务器采用传统无线电和计算技术。所提出的系统采用以发射机为中心的信息论准则进行设计，旨在降低通信开销，同时保留对端到端语义任务最相关的信息。在标准数据集上的数值结果验证了所提出的架构，并报告了一个初步测试平台的实现情况。
英文摘要：
An important use case of next-generation wireless systems is device-edge co-inference, where a semantic task is partitioned between a device and an edge server. The device carries out data collection and partial processing of the data, while the remote server completes the given task based on information received from the device. It is often required that processing and communication be run as efficiently as possible at the device, while more computing resources are available at the edge. To address such scenarios, we introduce a new system solution, termed neuromorphic wireless device-edge co-inference. According to it, the device runs sensing, processing, and communication units using neuromorphic hardware, while the server employs conventional radio and computing technologies. The proposed system is designed using a transmitter-centric information-theoretic criterion that targets a reduction of the communication overhead, while retaining the most relevant information for the end-to-end semantic task of interest. Numerical results on standard data sets validate the proposed architecture, and a preliminary testbed realization is reported.
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。
