Qiangfei Xia Abstract:
It becomes increasingly difficult to improve the speed-energy efficiency of traditional digital processors because of limitations in transistor scaling and the von Neumann architecture. To address this issue, computing systems inspired by the extremely high energy efficiency of the brain offer an attractive solution. In this short course, I will first introduce a few emerging devices using resistance as the state variable, including phase change memory (PCM), memristor/resistance switch (RRAM), magnetoresistance switch (MRAM) and ferroelectric tunnel junction (FTJ). I will then address how these devices can be integrated into large arrays to implement artificial neural networks for machine learning applications. Finally, I will discuss a few novel concepts, in particular devices that can faithfully emulate bio-realistic synaptic and neuronal functions as building blocks for spiking neural networks.
Bio:
Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA 01003
Dr. Xia is a professor of Electrical & Computer Engineering at UMass Amherst and head of the Nanodevices and Integrated Systems Lab (http://nano.ecs.umass.edu). He received his Ph.D. in Electrical Engineering in 2007 from Princeton University, and spent three years at the Hewlett-Packard Laboratories before joining UMass. Dr. Xia’s research interests include beyond-CMOS devices, integrated systems and enabling technologies, with applications in machine intelligence, reconfigurable RF systems and hardware security. He is a recipient of DARPA Young Faculty Award, NSF CAREER Award, and the Barbara H. and Joseph I. Goldstein Outstanding Junior Faculty Award.