Energy-Efficient Neuromorphic Chips for Real-Time Robotic Control: A Review

Neuromorphic computing has gained increasing attention as a bio-inspired solution to the limitations of traditional computing systems in power and real-time constraints. In robotic control tasks, real-time processing and energy efficiency are crucial, especially for autonomous and mobile systems. Neuromorphic chips mimic the structure and operation of biological neurons, enabling low-latency and low-power processing. This review examines the architectures, performance benchmarks, and application cases of mainstream neuromorphic hardware platforms used in robotic perception and control. It also compare their energy consumption and latency with conventional control platforms. Furthermore, this paper identifies key challenges in system integration and suggests future directions including improved scalability, online learning, and the combination with edge AI frameworks. The paper finds that neuromorphic chips can significantly reduce energy consumption and improve real-time performance in robotic control. However, challenges in algorithm adaptation, standardization, and hardware integration remain. This study aims to provide researchers with insights into the practical implementation of neuromorphic control systems in energy-sensitive robotic applications.