让边缘AI更省电：用自适应脉冲网络与硬件协同优化
Energy-Efficient Neuromorphic Computing for Edge AI: A Framework with Adaptive Spiking Neural Networks and Hardware-Aware Optimization
- 采用时序编码融合脉冲频率与时间模式，降低脉冲活动
- 在边缘设备上实现91%-96%准确率，延迟仅2.3毫秒
- 适合对能效和实时性要求高的边缘智能场景
边缘AI应用日益需要超低功耗、低延迟推理。基于事件驱动脉冲神经网络（SNNs）的类脑计算提供了一条有吸引力的路径，但受限于训练难度、硬件映射开销以及对时序动态的敏感性，实际部署仍受制约。我们提出NeuEdge框架，结合自适应SNN模型与硬件感知优化，用于边缘部署。NeuEdge采用一种时序编码方案，融合脉冲频率与脉冲时间模式，在降低脉冲活动的同时保持精度；并引入硬件感知训练流程，联合优化网络结构与芯片内布局，提升类脑处理器利用率。自适应阈值机制根据输入统计调整神经元兴奋性，降低能耗而不影响性能。在标准视觉与音频基准测试中，NeuEdge在边缘硬件上实现91%-96%准确率，推理延迟最高为2.3毫秒，估计能效达847 GOp/s/W。针对自主无人机任务的案例研究显示，相比传统深度神经网络，能耗降低最多达312倍，同时保持实时运行。
原文摘要 · Abstract (English)
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.
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