Neuromorphic Computing Architectures for Enhanced Signal Processing in Autonomous Systems

摘要

Innovative computing techniques that strike a balance between speed, efficiency, and adaptability are required due to the growing complexity of signal processing in autonomous systems. Neuromorphic computing, which draws inspiration from the structure of the human brain, offers a workable method for enhancing signal processing in real-time analysis and decision-making devices, including robotic systems, drones, and driverless cars. The integration of neuromorphic architectures for sophisticated signal processing in robotic systems, drones, and autonomous cars is examined in this research. The application of event-driven processing and spiking neural networks (SNNs) for real-time analysis and decision-making is the main emphasis of the research. The approach involves developing a scalable neuromorphic framework that makes use of cutting-edge hardware, including neuromorphic chips, and evaluating its reaction time, energy efficiency, and processing speed in comparison to traditional von Neumann architectures. Neuromorphic systems demonstrate their potential for continuous real-time data handling by reducing latency by 30% and improving energy efficiency by up to 50%. The system's resilience in handling intricate environmental signals and adjusting to changing inputs is highlighted by important findings. According to the paper's conclusion, incorporating neuromorphic designs greatly improves autonomous systems' efficiency and real-time processing capabilities, opening the door to more responsive and energy-efficient operations. Improving hardware compatibility and broadening applications across various autonomous technologies are two areas of future research.