Neuromorphic Computing for Sustainable Artificial Intelligence: A Review of Fundamental Principles

Review Article

Abstract

The rapid expansion of artificial intelligence (AI) has significantly increased computational demands, resulting in higher energy consumption and environmental concerns. Neuromorphic computing has emerged as a promising paradigm that emulates the structure and functionality of the human brain to achieve highly efficient, low-power information processing. Unlike conventional von Neumann architectures, neuromorphic systems integrate memory and computation while employing event-driven processing and massively parallel neural networks, thereby reducing latency and power consumption. This paper presents a comprehensive review of the fundamental principles, hardware architectures, learning mechanisms, and applications of neuromorphic computing in the context of sustainable AI. It examines the evolution of neuromorphic hardware, including spiking neural networks (SNNs), memristor-based devices, and specialized processors such as Intel Loihi and IBM TrueNorth, highlighting their advantages over traditional deep learning accelerators. The study further explores recent advancements in neuromorphic algorithms, edge intelligence, autonomous robotics, healthcare, Internet of Things (IoT), and real-time sensing applications, where energy-efficient computation is increasingly essential. Additionally, the paper discusses current challenges, including limited software ecosystems, training complexity, hardware scalability, standardization, and integration with existing AI frameworks. Emerging research directions involving hybrid AI models, brain-inspired cognitive architectures, quantum-neuromorphic systems, and sustainable edge computing are also examined. The review emphasizes that neuromorphic computing represents a transformative approach toward developing environmentally sustainable, scalable, and intelligent computing systems capable of meeting future AI demands while significantly reducing computational energy requirements.