Titolo: Edge AI in nature: insect-inspired neuromorphic Reflex Islands for safety-critical edge systems

Descrizione: Insects achieve millisecond sensor–motor loops with tiny sensors, compact neural circuits, and powerful actuators, embodying the principles of Edge AI [1–9]. We present a comprehensive architectural blueprint translating insect neurobiology into a hardware–software 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 utilizing spintronic primitives, specifically MRAM synapses (for non-volatile, innate memory) and spin-torque nano-oscillator (STNO) reservoirs (for temporal processing), to enable instant-on, memory-centric reflexes [10–16]. Furthermore, we formalize the biological governance mechanisms, demonstrating that unlike conventional ICEs and miniturbines that exhibit narrow best-efficiency islands, insects utilize active thermoregulation and DGC (Discontinuous Gas Exchange) to maintain nearly constant energy efficiency across a broad operational load by actively managing their thermal set-point, which we map into thermal-debt and burst-budget controllers [17–33]. We instantiate this integrated bio-inspired model in an insect-like IFEVS thruster, a solar cargo e-bike with a neuromorphic safety shell, and other safety-critical edge systems, providing concrete efficiency comparisons, latency and energy budgets, and safety-case hooks that support certification and adoption across autonomous domains [6,11,14,28].

Autore: Pietro Perlo, Marco Dalmasso, Marco Biasiotto, Davide Penserini¹

Editore: Università degli Studi di Torino

Data: 2025

Gestione dei diritti: This work is licensed under CC BY 4.0

Identificatore: ISBN: 9788875903794