传统AI监管不适用于类脑计算，需匹配其硬件与学习特性
Governance at the Edge of Architecture: Regulating NeuroAI and Neuromorphic Systems
- 针对类脑神经网络的新型监管方法，结合硬件物理特性
- 强调保证与审计需随架构演进，而非套用旧标准
- 适合关注神经形态计算合规性的研究者与政策制定者
当前人工智能治理框架，包括准确率、延迟和能效等监管基准，是为在冯·诺依曼架构上静态训练的传统人工神经网络设计的。神经人工智能（NeuroAI）系统基于神经形态硬件，通过脉冲神经网络实现，打破了这些假设。本文分析了现有治理框架在应对NeuroAI时的局限性，主张保障与审计方法必须与这类架构同步演进，将传统监管指标与脑启发计算的物理特性、学习动态及具身效率相协调，以实现技术根基扎实的可靠性保证。
原文摘要 · Abstract (English)
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.
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