Neuromorphic AI Architectures for Energy-Efficient Autonomous Systems

Abstract

This manuscript explores the design, implementation, and evaluation of neuromorphic AI architectures tailored for energy‑efficient autonomous systems. Motivated by the severe power constraints in mobile robotics, unmanned aerial vehicles (UAVs), and edge‑deployed autonomous platforms, we investigate spiking neural network (SNN) models deployed on state‑of‑the‑art neuromorphic hardware—specifically Intel Loihi, IBM TrueNorth, and the SpiNNaker platform. A mixed‑methods approach is adopted, combining theoretical analysis, statistical evaluation, and large‑scale simulation experiments. We develop three canonical SNN architectures (feedforward, convolutional, and recurrent) optimized via event‑driven encoding and homeostatic plasticity rules. Statistical comparisons against equivalent deep learning baselines demonstrate up to 85% reduction in energy consumption per inference while maintaining 92–97% of task accuracy across vision recognition and control tasks. Simulation research further examines latency, throughput, and robustness under variable sensor noise. The results confirm that neuromorphic solutions offer a compelling pathway to extend mission duration and reduce thermal budgets in autonomous systems. We conclude with recommendations for future hardware‑software co‑design and highlight open research directions in adaptive learning, hybrid analog‑digital integration, and on‑chip learning for real‑time autonomy.