On-Chip AI Processing in Neuromorphic Robot Controllers

Abstract

On-chip artificial intelligence (AI) processing implemented within neuromorphic robot controllers represents a paradigm shift in autonomous robotics, enabling ultralow-latency sensorimotor loops, drastic reductions in energy consumption, and adaptive behavior through biologically inspired spiking neural networks (SNNs). This manuscript delivers an in-depth exploration of on-chip AI for neuromorphic controllers, structured as follows: an expansive introduction to the motivation and context; a comprehensive literature review tracing the evolution of neuromorphic hardware and its robotic applications; a detailed Statistical Analysis section (including one comparative table) benchmarking neuromorphic versus conventional controllers on latency, power, and accuracy; a rigorous methodology outlining network architectures, simulation environments, and measurement protocols; extensive results highlighting significant performance gains; a substantive conclusion synthesizing findings; and a thorough scope and limitations discussion identifying future research directions. Through simulation-based experiments using Intel’s Loihi chip and an ARM Cortex-M4 microcontroller baseline, we demonstrate that on-chip neuromorphic processing reduces control-loop latency by approximately 68%, lowers power consumption by 83%, and modestly enhances inference accuracy. We contextualize these gains within real-world robotics, discuss implementation challenges such as hardware variability and integration complexity, and propose paths for incorporating on-chip learning. This expanded treatment underscores the transformative potential of neuromorphic AI for real-time, energy-constrained autonomy in robotics.