架构边缘的治理：规范神经AI与神经形态系统
Governance at the Edge of Architecture: Regulating NeuroAI and Neuromorphic Systems
AI总结：
本文探讨了神经AI系统在传统AI治理框架下的局限性，提出需与架构共同进化以实现技术基础的保证。
摘要：
当前的AI治理框架，包括针对准确性、延迟和能耗的监管基准，是为在冯·诺依曼硬件上运行的静态、集中训练的人工神经网络而设计的。神经AI系统，体现在神经形态硬件上并通过脉冲神经网络实现，打破了这些假设。本文探讨了当前AI治理框架在神经AI方面的局限性，认为保证和审计方法必须与这些架构共同进化，使传统监管指标与脑启发计算的物理特性、学习动态和具身体验效率对齐，以实现技术基础的保证。
英文摘要：
Current AI governance frameworks, including regulatory benchmarks for accuracy, latency, and energy efficiency, are built for static, centrally trained artificial neural networks on von Neumann hardware. NeuroAI systems, embodied in neuromorphic hardware and implemented via spiking neural networks, break these assumptions. This paper examines the limitations of current AI governance frameworks for NeuroAI, arguing that assurance and audit methods must co-evolve with these architectures, aligning traditional regulatory metrics with the physics, learning dynamics, and embodied efficiency of brain-inspired computation to enable technically grounded assurance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
