##基于神经形态计算的实时视觉处理

Abstract

This paper proposes a novel approach to real-time visual processing leveraging the principles of neuromorphic computing. The core aim is to design and implement a system capable of efficient image recognition and processing with reduced power consumption. The methodology centers on mimicking the biological visual system's neural network structure and dynamics through neuromorphic models. These models are then implemented utilizing hardware acceleration techniques to achieve real-time performance. The system's architecture is designed to overcome the limitations of traditional von Neumann architectures in image processing by exploiting inherent parallelism and energy efficiency found in biological neural systems. This research introduces a new paradigm for visual processing, offering a potentially transformative solution for applications demanding low-latency and low-power operation, such as autonomous robotics, surveillance, and edge computing. The system's performance is evaluated through simulation and theoretical analysis, demonstrating its potential for achieving significant improvements in processing speed and energy efficiency compared to conventional approaches.