Safety of self-assembled neuromorphic hardware

TL;DR

This paper discusses the safety considerations of neuromorphic hardware, emphasizing its potential for scalable, brain-inspired computing and the need for safety research to adapt to this emerging paradigm.

Contribution

It highlights the importance of integrating safety and interpretability into neuromorphic hardware development for reliable AI at scale.

Findings

Neuromorphic hardware offers scalable, brain-inspired computing.

Safety research should expand to include neuromorphic systems.

Supporting NMH safety can enhance trustworthy AI deployment.

Abstract

The scalability of modern computing hardware is limited by physical bottlenecks and high energy consumption. These limitations could be addressed by neuromorphic hardware (NMH) which is inspired by the human brain. NMH enables physically built-in capabilities of information processing at the hardware level. In other words, brain-like features bias hardware towards intelligence at scale. Neuropmorphic computing paradigms require a novel approach to safe, interpretable AI. In order to effectively engage the risk of misaligned AI, safety research may need to expand its scope to include NMH. This may be best achieved by supporting those currently engaged in NMH capability research to work on safety and related areas.