The rapid development in the field of artificial intelligence has relied principally on the advances in computing hardware However, their system scale and energy efficiency are still limited compared to the brain Memristor or redox resistive switch, provides a novel circuit building block that may address these challenges in neuromorphic computing and machine learning. In this talk, I will first briefly introduce the promises and challenges with regards to the use of memristors in realizing bio inspired computing. Secondly, I will show examples of memristor based neuromorphic computing Novel memristors have been used to simulate certain synaptic and neural dynamics, which led to prototypical hardware spiking neural networks practicing local learning rules and reservoir computing Thirdly, I will discuss the 128 × 64 1 transistor 1 memristor array for hardware accelerating machine learning This prototypical processing in memory system implemented deep Q reinforcement learning for control problems, as well as supervised training of convolutional and/or recurrent networks for classification.
Biography
Dr Zhongrui Wang is an assistant professor with the department of Electrical and Electronic Engineering at the University of Hong Kong Prior to joining HKU, Dr Wang received both B Eng (First class Honor) and Ph D from Nanyang Technological University in 2009 and 2014 respectively He did his postdoctoral research at University of Massachusetts Amherst His research interest lies in emerging memory based neuromorphic computing and machine learning, as well as modelling memristive materials using density functional theory He has authored and coauthored over 50 technical papers, including first authored articles on Nature Review Materials, Nature Materials, Nature Electronics, Nature Machine Intelligence, and Nature Communications, which have received 4200 citations His results have also been reported more than 40 times by mainstream scientific and popular media sources