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.

下一代无线系统的重要应用是设备边缘协同推理，其中语义任务在设备和边缘服务器之间进行划分。该论文提出了一种新的系统解决方案，称为神经形态的无线设备边缘协同推理，旨在用神经形态硬件运行设备的感知、处理和通信单元，而服务器使用传统的无线电和计算技术，并通过以发射机为中心的信息论准则设计，以减少通信开销，同时保留与感兴趣的端到端语义任务相关联的最重要信息。标准数据集的数值结果验证了所提出的架构，并报告了初步的实验测试。

通过有向信息瓶颈的神经形态学无线设备边缘协同推断