Researchers are increasingly focusing on the challenges of managing NeuroAI and neuromorphic systems, an area where current regulatory approaches are inadequate. Afifa Kashif of the University of Cambridge, Abdul Muhsin Hameed of the University of Washington, Asim Iqbal of Cornell University and colleagues have demonstrated that existing governance frameworks designed for static artificial neural networks running on traditional hardware are inadequate for these fundamentally different architectures. This paper highlights the critical need to re-evaluate assurance and auditing methods and advocates co-evolving regulations in parallel with brain-inspired computation to ensure technically sound and effective oversight of NeuroAI’s unique physics, learning dynamics, and efficiency. While NeuroAI promises significant advances in energy efficiency and real-time processing, realizing its potential safely and responsibly requires careful management, so it is important to understand these limitations and propose adaptive governance.
Neuromorphic systems and the inadequacies of current AI governance frameworks require proactive and adaptive regulatory approaches
Scientists are redefining the boundaries of artificial intelligence governance with research into the NeuroAI system. These new systems are built on neuromorphic hardware and utilize spiking neural networks, challenging the assumptions underlying current regulatory benchmarks for accuracy, latency, and energy efficiency.
This study examines the limitations of existing AI governance frameworks when applied to NeuroAI and suggests that assurance and audit methodologies need to evolve alongside these advanced architectures. This study details how important it is to align traditional regulatory metrics with the unique physics, learning dynamics, and inherent efficiency of brain calculations for technically grounded assurance.
NeuroAI represents the fusion of neuroscience and artificial intelligence, and aims to leverage insights from the brain to create smarter, more efficient systems. Neuromorphic computing moves away from traditional Neumann architectures by integrating memory and computation and operating in an asynchronous and event-driven manner.
At the algorithmic level, this manifests itself in spiking neural networks. Spiking neural networks communicate through discrete spikes that encode rate and timing information and often employ local learning rules such as spike-timing-dependent plasticity. This is in contrast to artificial neural networks that rely on continuous activation and global error backpropagation, such as the one that powers ChatGPT.
Overall, NeuroAI bridges the gap between scientific understanding and hardware implementation and signals a paradigm shift in neural computing. Neuromorphic event-driven vision systems are currently under development for implantable health monitors. The system can detect neurological or cardiovascular abnormalities in real-time with very low power requirements suitable for continuous edge deployment.
This study highlights the need for governance to keep pace with such advances and incorporate safety and social impact into the design of algorithms and hardware from the beginning. Recent global efforts to regulate AI, such as the EU AI Act, the US NIST AI Risk Management Framework, and China’s AI Safety Governance Framework, are primarily designed for static, high-computing, centrally trained models.
This study shows that these frameworks struggle to capture the adaptive and event-driven behavior of neuromorphic and NeuroAI systems. This study proposes a new approach represented by frameworks such as NeuroBench. This ties algorithm performance to hardware efficiency and reframes evaluation from raw computing to system-level auditing. In this paper, we consider how metrics of efficiency, adaptability, and embodiment can be translated into regulatory language to facilitate the responsible transition of NeuroAI from laboratory prototypes to real-world applications.
Researchers now have easy access to spiking neural network evaluation using the NeuroBench framework
Neuromorphic computing, which embodies the fusion of neuroscience and artificial intelligence, departs from traditional von Neumann architectures by co-locating memory and computation and operating asynchronously and event-driven. This shift is accomplished algorithmically through spiking neural networks that utilize discrete spikes to encode rate and timing information, often incorporating local learning rules such as spike timing-dependent plasticity.
These networks are in contrast to the artificial neural networks that rely on continuous activation and global error backpropagation, which form the basis of modern deep learning. This study highlights the critical need for co-evolution between AI governance and NeuroAI architectures, as current regulatory benchmarks for accuracy, latency, and energy efficiency are designed for static, centrally trained systems.
To address this, this study references NeuroBench, a device-independent framework that evaluates performance across task, model, and platform layers. NeuroBench leverages open reference code to facilitate system-level audits that link algorithm performance and hardware efficiency, reporting accuracy along with latency, power consumption, and energy per sample.
This study extends the foundations of NeuroBench and considers translating metrics of efficiency, adaptability, and embodiment into regulatory language. This analysis shows that existing AI governance frameworks created for static high-computing models struggle to capture the adaptive and event-driven behavior inherent in neuromorphic and NeuroAI systems.
Assurance and audit methods must therefore align traditional regulatory metrics with the underlying physics and learning dynamics of brain-inspired computation to enable a responsible transition from laboratory prototypes to real-world applications. This study recognizes the need for a comprehensive global understanding of AI governance and considers both binding laws and non-binding guidelines that establish enforceable obligations.
Neuromorphic computing and the inadequacies of existing AI governance metrics require new evaluation frameworks
Current governance frameworks, including regulatory benchmarks for accuracy, latency, and energy efficiency, are designed for statically and centrally trained artificial neural networks on von Neumann hardware. The NeuroAI system is implemented via a spiking neural network on neuromorphic hardware and challenges these established assumptions.
This study highlights the limitations within current governance frameworks when applied to NeuroAI and argues that assurance and audit methods need to evolve in line with these architectures. A centralized results schema ensures that all submissions include standardized metrics and metadata, enabling reproducible cross-device comparisons and transparent assessment of neuromorphic progression.
Computational accounting currently serves as the primary proxy for capabilities and risk, with EU AI law defining a systemic risk threshold of approximately 1025 FLOPs. The 2023 U.S. Executive Order requires reporting of training runs exceeding 1026 FLOPs, and similar restrictions have been adopted in the export control regime.
However, neuromorphic processors do not follow this assumption and utilize event-driven sparse computations measured in spikes per second rather than synchronous floating-point operations. Energy consumption is proportional to the number of meaningful events, not clock cycles. This study shows that neuromorphic hardware is able to bypass existing AI regulations due to fundamental differences in behavior.
Traditional governance assumes that more FLOPs equals greater capacity and risk, but this assumption is invalidated by the event-driven nature of neuromorphic systems. A 3 billion parameter transformer decoder LLM derived from IBM’s Granite-8B-Code-Base model and quantized to INT4 weights and activations was used for benchmarking.
NorthPole achieves significantly higher energy efficiency and lower latency than metrics based solely on FLOPs, while consuming much less power. This study highlights that reconstructing network attention, such as heatmaps, requires correlating spike trains and microsecond-scale timestamps across millions of neurons, yielding results that are dynamic, non-stationary, and difficult to interpret.
Auditability of NeuroAI requires dynamic systems analysis, particularly characterization of attractor landscapes, oscillatory coupling, spike synchrony, and stability margins, rather than static weight maps. Reproducing experiments within neuromorphic systems is delicate, as microscopic weight differences can alter the dynamics of the entire network, invalidating traditional auditing paradigms.
Neuromorphic systems require modified governance approaches to address unique ethical and security challenges
Current artificial intelligence governance frameworks designed for traditional computing systems are ill-suited for neuro-AI systems built on neuromorphic hardware. Traditional regulatory benchmarks focused on accuracy, latency, and energy efficiency do not match the unique properties of these brain-inspired architectures.
NeuroAI’s continuous learning cycle and interaction with real-world data make established methods of partitioning and auditing datasets impractical, effectively blurring the lines between model development and deployment. This misalignment is due to fundamental differences in the way these systems operate.
Unlike traditional AI, neuroAI continuously learns, adapts locally, and utilizes distributed memory integrated into the hardware, which creates challenges when auditing, exporting, and benchmarking. This impact is especially important in high-stakes applications such as medical devices and self-driving cars, where safety and accountability are paramount.
Existing AI risk assessments, including those used for export controls, may also not accurately assess neuromorphic chips due to different computational characteristics. The authors acknowledge that this analysis focuses on a subset of AI governance frameworks and provides only a limited exploration of sector-specific regulations.
As neuromorphic computing moves from the laboratory to real-world applications, addressing these governance gaps becomes increasingly urgent. Future efforts should focus on developing assurance and audit methods that co-evolve with neuroAI architectures, aligning regulatory metrics with the underlying physics and learning dynamics of brain-driven computation. This will require a transition from hypothetical discussions to real-world implementations, especially as these systems power safety-critical applications and the neuromorphic chip supply chain matures.
