Brain-inspired algorithms such as artificial neural networks (ANN) have been deployed for a variety of artificial intelligence (AI) applications. These algorithms are often implemented on a conventional computer with a von Neumann architecture, which, unlike the brain, physically separates memory and processing units. This not only slows them down, but also raises their energy consumption significantly. Neuromorphic computing addresses these issues by drawing inspiration from the brain to design time- and energy-efficient hardware for data processing. In this talk, I will firstly briefly discuss the developments and challenges in neuromorphic computing based on emerging memory devices. I will then discuss my past and current work on the exploration of memory devices with new materials and bio-inspired computing algorithms: first, memristor and optical memory based on two-dimensional materials, second, physically reconstructing biological neuronal connectivity from neuronal recordings using memristor, last, spatial–temporal data processing with an optical memory-based reservoir computing.
Mr. Renjing Xu has been a graduate student in Applied Physics at Harvard University since August 2016. From August 2015 to February 2016, he was a visiting scholar at the University of Wisconsin – Madison's Photonics Lab. He graduated from the Australian National University with a bachelor's degree in photonic systems in 2015. His research interest focuses on achieving energy-and time-efficient neuromorphic computing with the development of novel devices and bio-inspired algorithms. In 2017, he was awarded the Analog Devices Outstanding Student Designer Award. He has authored over 10 technical papers and received more than 1250 citations. His findings on the world's thinnest lens and phosphorene have been covered by more than 100 mainstream and web media sites.
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