Towards brain-inspired edge AI: a review of memristor-based neuromorphic computing and learning algorithms

Abstract With the advent of innovative technologies and the emerging demands for in situ edge decision-making, the weaknesses of the conventional von Neumann computing paradigm have become increasingly evident. While such architectures were considered reliable until recently, they have struggled to meet the growing requirements for greater processing speed, lower power consumption, and enhanced scalability. To highlight these challenges, several studies since the early 2010s, have focused on neuromorphic computing based on memristors. Unlike traditional computing, this approach relies more on how the human brain processes information, offering a promising alternative for future edge AI applications. Memristor-based systems, particularly those employing crossbar architectures like 1T1R and 1R arrays, enable analog computation and efficient matrix-vector multiplication, laying the foundation for energy-efficient, scalable hardware. This comprehensive review paper explores the fundamentals of memristor, neuromorphic computing with memristor, learning algorithms focusing on Artificial Neural Networks (ANN), Binary Neural Networks (BNN), and Spiking Neural Networks (SNN), while exploring other learning models as well by comparing the accuracy percentage of each model and edge AI tasks such as image classification, emotion recognition, and multimodal learning. Furthermore, it addresses the intrinsic advantages of computation-in-memory architecture regarding scalability, latency reduction, and parallelism. It also offers insights into the challenges such as device-to-device variability, low endurance, non-ideal switching properties, and compatibility with complementary metal–oxide–semiconductor (CMOS) technologies. This study flow will help understand each technique’s central concept to highlight future development challenges, discussing the status and prospects of neuromorphic and brain-inspired computing technologies.