Edge AI applications increasingly require ultra-low-power, low-latency inference. Neuromorphic computing based on event-driven spiking neural networks (SNNs) offers an attractive path, but practical deployment on resource-constrained devices is limited by training difficulty, hardware-mapping overheads, and sensitivity to temporal dynamics. We present NeuEdge, a framework that combines adaptive SNN models with hardware-aware optimization for edge deployment. NeuEdge uses a temporal coding scheme that blends rate and spike-timing patterns to reduce spike activity while preserving accuracy, and a hardware-aware training procedure that co-optimizes network structure and on-chip placement to improve utilization on neuromorphic processors. An adaptive threshold mechanism adjusts neuron excitability from input statistics, reducing energy consumption without degrading performance. Across standard vision and audio benchmarks, NeuEdge achieves 91-96% accuracy with up to 2.3 ms inference latency on edge hardware and an estimated 847 GOp/s/W energy efficiency. A case study on an autonomous-drone workload shows up to 312x energy savings relative to conventional deep neural networks while maintaining real-time operation.

翻译：边缘AI应用日益需要超低功耗、低延迟的推理能力。基于事件驱动脉冲神经网络（SNNs）的神经形态计算提供了一条极具前景的技术路径，但其在资源受限设备上的实际部署仍受限于训练难度、硬件映射开销以及对时序动态的敏感性。本文提出NeuEdge框架，该框架将自适应SNN模型与面向边缘部署的硬件感知优化相结合。NeuEdge采用一种融合发放率与脉冲时序模式的时序编码方案，在保持精度的同时降低脉冲活动量；并提出一种硬件感知训练流程，协同优化网络结构与片上布局，以提升神经形态处理器的资源利用率。其自适应阈值机制根据输入统计特性动态调整神经元兴奋性，从而在不损失性能的前提下降低能耗。在标准视觉与音频基准测试中，NeuEdge在边缘硬件上实现了91-96%的准确率，推理延迟低至2.3毫秒，能效估计达到847 GOp/s/W。一项针对自主无人机工作负载的案例研究表明，相较于传统深度神经网络，NeuEdge在保持实时运行的同时，可实现高达312倍的节能效果。