Ultra-low-energy skyrmion-based learning automata element for adaptive edge intelligence

Magnetic skyrmions are nanoscale, topologically protected spin textures that offer exceptional stability, non-volatility, and ultra-low energy manipulation, making them attractive candidates for next-generation computing devices. Their controllable motion in ferromagnet/heavy-metal bilayers enables robust binary state encoding, opening opportunities for energy-efficient decision-making hardware. As adaptive decision-making models, learning automata can benefit from device-level integration, enabling direct in-memory learning with minimal power consumption. This work implements a skyrmion-based learning automata element that maps finite-state transitions to skyrmion motion along nanoscale tracks. The skyrmion's lateral position represents each automaton state ('include' or 'exclude'), and transitions are driven by spin-orbit torque under optimized current densities. This element is demonstrated within the framework of a Tsetlin Machine, providing a hardware-efficient and interpretable logic-learning mechanism. Micromagnetic simulations in MuMax3, utilizing a Co/Pt bilayer, confirm the stable nucleation, motion, and detection of skyrmions. The proposed design achieves 4.86 aJ per state update, representing 99% energy reduction over comparable non-volatile memory-based automata, with a 5ns transition time. This work establishes a scalable and reconfigurable device-level building block for energy-efficient, edge-oriented machine intelligence.