The Impact of Neuromorphic Computing Architectures on the Implementation and Scalability of Artificial Intelligence Algorithms

Abstract

Neuromorphic systems are essential for developing contemporary applications, particularly in robotics and artificial intelligence. These systems overcome the drawbacks of conventional computing architectures by utilizing bio-inspired techniques to increase processing power. Event cameras, which offer excellent dynamic range and temporal resolution, show off the possibilities of these systems. By combining neuromorphic architectures with analog photonics, computational efficiency will be further improved, meeting the need for speed and flexibility when managing big datasets. More complex interactions between artificial intelligence and the actual world are made possible by neuromorphic systems, which enhance machine perception. However, hardware, data properties, and application domain all affect performance.  Neuromorphic architectures are anticipated to be crucial to the development of AI systems in the future, propelling advancements in a variety of sectors as research advances.