Neuromorphic Computing Architectures for Real-time Image Processing and Pattern Recognition

Abstract

Real-time image processing and pattern recognition applications have found a new paradigm in neuromorphic computing systems. In this paper, we quantitatively compare neuromorphic architecture performance to that of conventional computing techniques. We study processing speed, accuracy, and energy usage for diverse image processing jobs using a controlled experimental methodology. The outcomes highlight the advantages of the Neuromorphic architecture, which is distinguished by quicker processing times and greater precision. These results demonstrate the effectiveness of event-driven spiking neural networks and are consistent with earlier studies. Comparisons with hybrid architectures highlight the Neuromorphic architecture's strength as a stand-alone system and point to simpler implementations. However, issues with accuracy fluctuation and the requirement for scalability continue, emphasizing areas for more study. The energy economy of neuromorphic architectures makes them essential parts of real-time image processing and pattern recognition as the field develops.