Neuromorphic Computing for Real-Time Image Processing

Abstract

This paper investigates the application of neuromorphic computing to address the limitations of traditional von Neumann architectures in real-time image processing. The core argument presented is that the inherent bottleneck of separating memory and processing units in conventional computers significantly hinders the performance of computationally intensive image processing tasks. We propose leveraging neuromorphic chips and spiking neural networks (SNNs) as a solution. SNNs, inspired by the biological brain, offer the potential for drastically reduced power consumption and increased processing speeds compared to conventional digital systems. This work outlines the theoretical framework and key considerations for implementing image processing algorithms directly on neuromorphic hardware. Specifically, we explore the mapping of common image processing operations, such as edge detection and feature extraction, onto SNNs. The potential for achieving near-real-time performance with minimal energy expenditure is highlighted. Further research directions, including optimization strategies for SNN design and exploration of different neuromorphic architectures, are discussed.