AI 中文总结
针对新兴边缘智能系统中传感器在能量和频谱预算下向远程模型传输信息的问题，提出基于向量符号架构框架构建的NOMA-NC协议，用于并行远程推理，实验表明该协议能在不增设备传输能量的情况下提升吞吐量并节省计算能量。
AI 中文摘要
新兴的边缘智能系统越来越依赖始终在线的传感器密集部署，这些传感器必须在严格的能量和频谱预算下将与任务相关的信息传送给远程模型。事件驱动的神经形态传感与脉冲神经网络（SNN）的结合在这种情况下很有吸引力，因为它能产生动态稀疏表示，只有在有信息事件发生时才消耗能量用于通信和计算。此前用于远程推理的多址协议针对协作场景，而本文解决的是并行远程推理问题，即每个设备观察不同输入并需要自己的分类决策。我们提出了NOMA-NC，一种基于向量符号架构（VSA）框架的非正交多址（NOMA）神经形态通信（NC）协议。在NOMA-NC中，每个设备将其稀疏脉冲特征图与特定于设备的置换密钥绑定，一组中的所有设备同时传输，以便空中叠加直接实现VSA捆绑操作。一个共享解码SNN，连同轻量级的每个设备的学习解绑，在一次推理过程中恢复所有决策。在N-MNIST和DVS128手势数据集上的实验表明，NOMA-NC在接收端计算能量方面产生了吞吐量增益和节省，且与同时活跃设备的数量成次比例关系，同时不增加每个设备的传输能量。
英文摘要
Emerging edge intelligence systems increasingly rely on dense deployments of always-on sensors that must convey task-relevant information to a remote model under tight energy and spectral budgets. The deployment of event-driven neuromorphic sensing paired with spiking neural networks (SNNs) is attractive in this regime because it produces dynamically sparse representations, so that energy is spent on communication and computation only when informative events occur. Prior multiple-access protocols for remote inference using neuromorphic sensing and computing targeted collaborative settings, in which the server fuses information from all devices into a single decision. This paper instead addresses parallel remote inference, in which each device observes a distinct input, and requires its own classification decision. We propose NOMA-NC, a non-orthogonal multiple-access (NOMA) neuromorphic communication (NC) protocol built on the vector symbolic architecture (VSA) framework. In NOMA-NC, each device binds its sparse spike feature map with a device-specific permutation key, and all devices in a group transmit concurrently so that the over-the-air superposition directly realizes the VSA bundling operation. A shared decoding SNN, together with lightweight per-device learned unbinding, recovers all decisions in a single inference pass. Experiments on the N-MNIST and DVS128 Gesture datasets show that NOMA-NC yields goodput gains and savings in terms of receiver computing energy that are sub-proportional to the number of simultaneously active devices, without increasing the per-device transmission energy.
