Evolution of Neuromorphic Computing
The conventional Von Neumann architecture is explored and its shortcomings are outlined; neuromorphic architecture as an alternative and its evolution are described; and the key challenges hindering neuromorphic computing development are addressed.
Abstract
With the advancement of artificial intelligence (AI) technologies, novel and inventive approaches for addressing complex problems are coming to the forefront. Neuromorphic computing based on AI technologies stands as an exemplar, endeavoring to mimic the human brain’s intricate neural architecture and computational principles within electronic devices. Contrary to conventional Von Neumann architecture, neuromorphic computing architecture offers a promising solution for building intelligent and efficient computational systems that excel in tasks requiring low power consumption, real-time processing, and adaptability. Subsequently, it is employed in various applications such as robotics, sensory processing, neuromorphic vision, edge computing, etc. This paper explores the conventional Von Neumann architecture and outlines its shortcomings. Next, neuromorphic architecture as an alternative and its evolution are described. Next, the characteristics of neuromorphic computing and its diverse applications are illustrated. The paper also addresses the key challenges hindering neuromorphic computing development.