materials Review In-Memory Logic Operations and Neuromorphic Computing in Non-Volatile Random Access Memory Qiao-Feng Ou 1, Bang-Shu Xiong 1, Lei Yu 1, Jing Wen 1, Lei Wang 1,* and Yi Tong 2,* 1 School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China;
[email protected] (Q.-F.O.);
[email protected] (B.-S.X.);
[email protected] (L.Y.);
[email protected] (J.W.) 2 College of Electronic and Optical Engineering & College of Microelectronics, Nanjing University of Posts and Telecommunications, Nanjing 210023, China * Correspondence:
[email protected] (L.W.);
[email protected] (Y.T.) Received: 15 June 2020; Accepted: 6 August 2020; Published: 10 August 2020 Abstract: Recent progress in the development of artificial intelligence technologies, aided by deep learning algorithms, has led to an unprecedented revolution in neuromorphic circuits, bringing us ever closer to brain-like computers. However, the vast majority of advanced algorithms still have to run on conventional computers. Thus, their capacities are limited by what is known as the von-Neumann bottleneck, where the central processing unit for data computation and the main memory for data storage are separated. Emerging forms of non-volatile random access memory, such as ferroelectric random access memory, phase-change random access memory, magnetic random access memory, and resistive random access memory, are widely considered to offer the best prospect of circumventing the von-Neumann bottleneck. This is due to their ability to merge storage and computational operations, such as Boolean logic. This paper reviews the most common kinds of non-volatile random access memory and their physical principles, together with their relative pros and cons when compared with conventional CMOS-based circuits (Complementary Metal Oxide Semiconductor).