Neuromorphic Computing: Advancing Energy-Efficient AI Systems through Brain-Inspired Architectures
This review highlights recent advancements, ongoing research efforts, and potential future directions, illustrating how neuromorphic computing can redefine the landscape of AI by enabling systems that are not only faster and more efficient but also capable of real-time learning and decision-making in dynamic environments.
Abstract
Neuromorphic computing represents a transformative approach to artificial intelligence, leveraging brain-inspired architectures to enhance energy efficiency and computational performance. This paper explores the principles and innovations underlying neuromorphic systems, which mimic the neural structures and processes of biological brains. We discuss the advantages of these architectures in processing information more efficiently than traditional von Neumann models, particularly in tasks involving pattern recognition, sensory processing, and adaptive learning. By integrating concepts from neuroscience with cutting-edge hardware developments, such as spiking neural networks and memristors, neuromorphic computing addresses the critical challenges of power consumption and scalability in AI applications. This review highlights recent advancements, ongoing research efforts, and potential future directions, illustrating how neuromorphic computing can redefine the landscape of AI by enabling systems that are not only faster and more efficient but also capable of real-time learning and decision-making in dynamic environments.