Learning with Resistive Switching Neural Networks

With the slowdown of Moore's law and the intensification of memory wall as well as von-Neumann bottleneck, processing-in-memory with emerging non-volatile analog devices, such as RRAMs or memristors, is a potential solution to accelerate machine learning in hardware neural networks, which may drastically improve the energy-area efficiency. In this paper, we discuss three major types of learning, namely the supervised, reinforcement, and unsupervised learning that are implemented with various 1-transistor-1-memristor (1T1R) based neural networks.