Neuromorphic Computing for Artificial Intelligence

Neuromorphic computing, an approach to building intelligent machines, is evolving into a method of computer engineering in which elements of a computer are modeled after systems in the human brain and nervous system. The term refers to the design of both hardware and software computing elements. Neuromorphic computing is sometimes referred to as neuromorphic engineering^[1]. Neuromorphic computing includes new algorithmic approaches that emulate how the human brain interacts with the world to deliver capabilities closer to human cognition.

Neuromorphic computing promises to open exciting new possibilities and is already in use in a variety of areas including, sensing, robotics, healthcare, and large-scale AI applications^[2] . Neuromorphic computing offers a means of designing AI systems capable of learning and adapting in a more similar way to human cognition than traditional AI algorithms. Neuromorphic computing aims to address the challenges of next-generation AI by providing a brain-inspired energy-efficient computing paradigm that primarily focuses on the ‘thinking’ and ‘processing’ side of these human-like systems. Inspired by the human brain, neuromorphic computing technologies have made important breakthroughs in recent years as alternatives to overcome the power and latency shortfalls of traditional digital computing^[2].