论文标题
低功率边缘的神经形态无线系统的端到端学习人工智能
End-to-End Learning of Neuromorphic Wireless Systems for Low-Power Edge Artificial Intelligence
论文作者
论文摘要
本文推出了一种基于神经形态传感，脉冲无线电（IR）和尖峰神经网络（SNNS）的新型“全石”低功率解决方案，用于远程无线推理。在拟议的系统中，事件驱动的神经形态传感器产生由SNN编码的异步时间编码的数据流，其输出尖峰信号是通过IR调制的，并通过一般频率选择性通道传输；尽管接收器的输入是通过对接收信号的硬检测获得的，并将其馈送到SNN进行分类。我们介绍了一种端到端的培训程序，该程序将编码器，通道和解码器的级联反应视为基于概率的基于SNN的自动编码器，该自动编码器实现了联合源通道编码（JSCC）。将所提出的系统称为NeuroJSCC，与常规同步框架和未编码的传输相比，就延迟和准确性而言。该实验证实，所提出的端到端神经形态边缘体系结构为高效且低延迟的遥感，通信和推理提供了有希望的框架。
This paper introduces a novel "all-spike" low-power solution for remote wireless inference that is based on neuromorphic sensing, Impulse Radio (IR), and Spiking Neural Networks (SNNs). In the proposed system, event-driven neuromorphic sensors produce asynchronous time-encoded data streams that are encoded by an SNN, whose output spiking signals are pulse modulated via IR and transmitted over general frequence-selective channels; while the receiver's inputs are obtained via hard detection of the received signals and fed to an SNN for classification. We introduce an end-to-end training procedure that treats the cascade of encoder, channel, and decoder as a probabilistic SNN-based autoencoder that implements Joint Source-Channel Coding (JSCC). The proposed system, termed NeuroJSCC, is compared to conventional synchronous frame-based and uncoded transmissions in terms of latency and accuracy. The experiments confirm that the proposed end-to-end neuromorphic edge architecture provides a promising framework for efficient and low-latency remote sensing, communication, and inference.
