Artificial intelligence (AI) will enable machines to think and solve complex tasks like human beings. In recent years, artificial neural networks have improved recognition and classification accuracy. However, state-of-the-art deep learning algorithms require large network models with multiple layers, which pose significant challenges for complementary metal-oxide-semiconductor (CMOS) implementation due to limitations in conjoining computation, memory, and communication requirements in large networks. As an alternative hardware platform, resistive devices (memristor) have been proposed for weight storage and fast parallel neural computing with low power consumption. The paralleism property of the resistive corssbar arrays for matrix-vector multiplication enables significant acceleration of core neural computations. In this talk, Dr. Choi will present a systematic study on the fundamental understanding of resistive switching devices. He will also introduce the appliation of memristor crossbar network in neuromorphic computing through dataclustering based on unsupervised learning, which suggests potential applications of memristor network for effective data classification for solving real-world problems. In addition, he will introduce epitaxial RAM or epiRAM, a recently developed resistive switching devices, which possesses all the required characteristics for neuromorphic computing. The epiRAM, single-crystalline-based memristive devices, show precise weight controls with a superior online learning accuracy of 95.1% – a key step paving the way towards post von Neumann computing. Finally, he will discuss the projections and future direction of the memristor-based neuromorphic computing system.