Edge AI in Nature: Insect-Inspired Neuromorphic Reflex Islands for Safety-Critical Edge Systems

Insects achieve millisecond sensor–motor loops with tiny sensors, compact neural circuits, and powerful actuators, embodying the principles of Edge AI long before electronics existed [1–9]. In this perspective, we treat insects as canonical edge-AI systems and translate their neurobiology and physiology into a concrete engineering stack: a latency-first control hierarchy that partitions tasks between a fast, dedicated Reflex Tier and a slower, robust Policy Tier, with explicit WCET envelopes and freedom-from-interference boundaries [1–9]. This architecture is realized through a neuromorphic Reflex Island built from spintronic and neuromorphic primitives, MRAM synapses for non-volatile, innate reflex memory, and spin-torque nano-oscillator (STNO) reservoirs for temporal processing—yielding instant-on, memory-centric reflexes compatible with emerging industrial roadmaps [10–16,56,62–67].We further formalize the thermoregulatory and respiratory strategies that allow insects to maintain nearly constant mechanical efficiency across a wide load range: active thoracic temperature control and Discontinuous Gas Exchange (DGC) [17–33]. These mechanisms motivate firmware-level “thermal-debt” and burst-budget controllers, contrasting sharply with the narrow best-efficiency islands of internal combustion engines and miniturbines [34–43]. We instantiate this integrated bio-inspired model in two concrete edge systems: an insect-like IFEVS thruster with nearly flat-band thermal efficiency over thrust, and a solar-assisted cargo e-bike equipped with an insect-inspired neuromorphic safety shell [6,11,14,28,58–61]. Across these examples we provide efficiency comparisons, latency and energy budgets, and safety-case hooks (fault taxonomies, WCET envelopes) aimed at guiding adoption in safety-critical domains.