Memristor-based Energy-Efficient Neuromorphic Computing

In the past decade, the rapid growth of artificial intelligence demands for intelligent computing chips. However, the continuous increase of computing power and energy efficiency for conventional chips face critical challenges from the slowdown of Moore's law scaling and also their von Neumann architecture. Inspired by human brain, computing-in- memory with emerging devices, such as memristors, has emerged as a promising neuromorphic paradigm to break the von Neumann bottleneck. Tremendous progress has been recently made in the developments of oxide-based memristors as neuromorphic devices, such as artificial synapses, neurons as well as dendrites. In this talk, I will first discuss the hardware challenges for artificial intelligence and then introduce the recent progress on the memristor-based computing-in-memory for neuromorphic computing, from material and device developments to process integration and chip demonstrations. Recent works on memristor-based signal processing for dendritic computing and reservoir computing will also be discussed. As the end, I will highlight future research directions and challenges for memristor-based neuromorphic computing.