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 augmented with emerging devices, in particular memristors, offer an attractive solution. A memristor, also known as a resistance switch, is an electronic device whose internal resistance state is dependent on the history of the current and/or voltage it has experienced. With their working mechanisms based on ion migration, the switching dynamics and electrical behavior of memristors closely resemble those of biological synapses and neurons. Because of its small size and fast switching speed, a memristor consumes a small amount of energy to update the internal state (training). Built into large-scale crossbar arrays, memristors perform in-memory computing by utilizing physical laws, such as Ohm’s law for multiplication and Kirchhoff’s current law for accumulation. The current readout at all columns (inference) is finished simultaneously regardless of the array size, offering a huge parallelism and hence superior computing throughput. The ability to directly interface with analog signals from sensors, without analog/digital conversion, could further reduce the processing time and energy overhead.

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I will introduce a high performance memristor that is the basis for our recent artificial neural networks, highlighting its two nanometer scalability and eight layer stackability. I will then showcase the integration of large memristor crossbar arrays for analog signal and image processing, and the implementation of multilayer memristor neural networks for machine learning applications. Finally, I will briefly introduce a diffusive memristor as a bio-realistic synapse and neuron emulator, and review further applications of memristors in reconfigurable radiofrequency systems and hardware security.

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Biography:
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). Before joining UMass, he spent three years at the Hewlett-Packard Laboratories where he demonstrated the first hybrid memristor/CMOS integrated circuits. He received his Ph.D. in Electrical Engineering in 2007 from Princeton University. 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.