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Prof. Bhavin Shastri

Queen's University
Date of Live Presentation: Wed, 20-Mar-2019
Location: Ryerson University


Brain-inspired artificial intelligence (AI) hardware using light


Artificial Intelligence (AI) is transforming our lives in the same way as the advent of the Internet and cellular phones has done. AI is revolutionizing the healthcare industry with complex medical data analysis, actualizing self-driving cars, and beating humans at strategy games such as Go. However, it takes thousands of CPUs and GPUs, and many weeks to train the neural networks in AI hardware. Over the last six years, this compute power has doubled every 3.5 months. Traditional CPUs, GPUs, and neuromorphic (i.e., brain-inspired) electronics such as the IBM TrueNorth and Google TPU will not be powerful enough to train the neural networks of the near future. There is a global race to solve this challenge. My research group is developing the next generation of AI hardware with neuromorphic photonics processors that use light instead of electric signals. By combining the high bandwidth and efficiency of photonic devices with the adaptive, parallelism and complexity similar to the brain, our processors have the potential to be at least ten thousand times faster than state-of-the-art electronic processors while consuming less energy. In this lecture, I will describe our work on building processors on a silicon photonics platform which enables large-scale integration of optical and electronic devices on the same substrate. I will demonstrate how our photonic neural networks can solve coupled ordinary differential equations (such as a Lorenz attractor or a nonlinear optimization problem) a thousand times faster than conventional CPUs. In summary, I will take a look at some of the traditional challenges of photonic information processing, describe the photonic neural-network approaches being developed by our lab and others, and offer a glimpse at the future outlook for this emerging field.

Short bio

Bhavin J. Shastri is an Assistant Professor of Engineering Physics at Queen’s University, Canada. He received the Honours B.Eng. (with distinction), M.Eng., and Ph.D. degrees in electrical engineering (photonics) from McGill University, Canada, in 2005, 2007, and 2012, respectively. He was an Associate Research Scholar (2016-2018) and a Banting and NSERC Postdoctoral Fellow (2012-2016) at Princeton University, USA. His research interests include nanophotonics, photonic integrated circuits, and neuromophic computing, with emphasis on applications such as information processing, nonlinear programming, and study of complex dynamical systems. Dr. Shastri is a co-author of the book, Neuromorphic Photonics (Taylor & Francis, CRC Press). He is a recipient of the 2014 Banting Postdoctoral Fellowship from the Government of Canada, the 2012 D. W. Ambridge Prize for the top graduating Ph.D. student, an IEEE Photonics Society 2011 Graduate Student Fellowship, a 2011 NSERC Postdoctoral Fellowship, a 2011 SPIE Scholarship in Optics and Photonics, a 2008 NSERC Alexander Graham Bell Canada Graduate Scholarship, and a 2007 Lorne Trottier Engineering Graduate Fellowship. He was the recipient of the Best Student Paper Awards at the 2010 IEEE Midwest Symposium on Circuits and Systems (MWSCAS), the 2004 IEEE Computer Society Lance Stafford Larson Outstanding Student Award, and the 2003

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