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.

翻译：尽管脉冲神经网络（SNNs）有望规避边缘智能面临的严苛尺寸、重量与功耗（SWaP）约束，但该领域目前面临“部署悖论”——将异步事件驱动动态映射到传统冯·诺依曼架构的低效性常常抵消理论上的能耗增益。本综述超越仅关注算法的还原论视角，采用严谨的系统级软硬件协同设计视角审视2020-2025年发展轨迹，聚焦于将生物合理性转化为硅基实现的“最后一公里”技术——从量化方法到混合架构。我们批判性剖析了训练复杂性（直接学习与转换的二分法）、制约状态神经元更新的“存储墙”瓶颈、以及神经形态编译工具链中的关键软件缺口之间的相互作用。最后，我们展望了解决根本性“同步-异步失配”的路线图，提出将标准化神经形态操作系统作为实现普适性、能量自洽的绿色认知基板的基础层。