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Governance at the Edge of Architecture: Regulating NeuroAI and Neuromorphic Systems

Afifah Kashif, Abdul Muhsin Hameed, Asim Iqbal

TL;DR

Current AI governance frameworks rely on compute thresholds around $10^{25}$ FLOPs for systemic risk and assume inspectable, static weights, which does not fit NeuroAI on neuromorphic hardware. The paper analyzes governance gaps and argues for co-evolution of assurance metrics with neuromorphic physics and learning dynamics, proposing energy-per-inference and a weight-change ratio (WTR) as runtime signals, alongside NeuroBench-style device benchmarks. It highlights four key gaps: FLOP-centric regulation, inspectable weights, data governance under continuous learning, and explainability in dynamic, distributed states. The proposed directions—architecture-aware governance, embedded governance primitives, and cross-border coordination—offer a path to responsible edge deployment of NeuroAI systems.

Abstract

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.

Governance at the Edge of Architecture: Regulating NeuroAI and Neuromorphic Systems

TL;DR

Current AI governance frameworks rely on compute thresholds around FLOPs for systemic risk and assume inspectable, static weights, which does not fit NeuroAI on neuromorphic hardware. The paper analyzes governance gaps and argues for co-evolution of assurance metrics with neuromorphic physics and learning dynamics, proposing energy-per-inference and a weight-change ratio (WTR) as runtime signals, alongside NeuroBench-style device benchmarks. It highlights four key gaps: FLOP-centric regulation, inspectable weights, data governance under continuous learning, and explainability in dynamic, distributed states. The proposed directions—architecture-aware governance, embedded governance primitives, and cross-border coordination—offer a path to responsible edge deployment of NeuroAI systems.

Abstract

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.
Paper Structure (19 sections, 1 figure, 1 table)

This paper contains 19 sections, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Current Regulatory Frameworks
  3. Binding Laws
  4. Non-Binding Guidelines
  5. Assurance Methodology
  6. Traditional Approaches
  7. NeuroAI and Neuromorphic Computing
  8. Governance Gaps
  9. Limits of FLOP-Centric Regulation
  10. The End of Inspectable Weights and Systems
  11. Data in the Dark
  12. Discussion
  13. Limitations
  14. Governance Takeaways
  15. High-Stakes Implications
  16. ...and 4 more sections

Figures (1)

  • Figure 1: NorthPole performs at$\sim$70× higher energy efficiency and $\sim$2.5× lower latencythan H100 while using a non-FLOP metric, illustrating why neuromorphic computing can elude compute-based thresholds.