解决脑启发AI在边缘设备部署中的能耗与效率难题
Brain-inspired AI for Edge Intelligence: a systematic review
- 从软硬件协同设计视角分析神经网络的异步映射问题
- 指出训练复杂性与内存瓶颈是当前主要技术障碍
- 适合关注边缘智能、类脑计算和芯片系统设计的研究者
脉冲神经网络（SNNs）有望突破边缘智能在尺寸、重量和功耗（SWaP）方面的限制，但当前面临‘部署悖论’：理论上的能耗优势常因将异步事件驱动行为映射到传统冯·诺依曼架构时的低效而被抵消。本综述采用系统级软硬件协同设计视角，梳理2020-2025年间的技术演进，聚焦从量化方法到混合架构等‘最后一公里’关键技术，推动生物合理性向硅基实现转化。我们深入剖析训练复杂性（直接学习与转换方法的权衡）、状态化神经元更新所受的‘内存墙’制约，以及类脑编译工具链中的关键软件缺口。最后，提出化解‘同步-异步不匹配’的根本路径，主张构建标准化类脑操作系统，作为实现无处不在、能量自给的绿色认知底座的基础层。
原文摘要 · Abstract (English)
While Spiking Neural Networks (SNNs) promise to circumvent the severe Size, Weight, and Power (SWaP) constraints of edge intelligence, the field currently faces a "Deployment Paradox" where theoretical energy gains are frequently negated by the inefficiencies of mapping asynchronous, event-driven dynamics onto traditional von Neumann substrates. Transcending the reductionism of algorithm-only reviews, this survey adopts a rigorous system-level hardware-software co-design perspective to examine the 2020-2025 trajectory, specifically targeting the "last mile" technologies - from quantization methodologies to hybrid architectures - that translate biological plausibility into silicon reality. We critically dissect the interplay between training complexity (the dichotomy of direct learning vs. conversion), the "memory wall" bottlenecking stateful neuronal updates, and the critical software gap in neuromorphic compilation toolchains. Finally, we envision a roadmap to reconcile the fundamental "Sync-Async Mismatch," proposing the development of a standardized Neuromorphic OS as the foundational layer for realizing a ubiquitous, energy-autonomous Green Cognitive Substrate.
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