Abstract: Advances in machine learning, notably deep learning, have led computers to match or surpass human performance in several cognitive tasks including vision, speech, and natural language processing. However, implementation of neural algorithms in conventional ​“von-Neumann” architectures are several orders of magnitude more area and power expensive than the biological brain.  We believe that exploring this new paradigm of computing necessitates a multi-disciplinary approach: exploration of new learning algorithms inspired from neuroscientific principles, developing network architectures best suited for such algorithms, new hardware techniques to achieve orders of improvement in energy consumption, and nanoscale devices that can closely mimic the neuronal and synaptic operations of the brain leading to a better match between the hardware substrate and the model of computation.

In this talk, I will focus on our recent works on neuromorphic computing with spike-based learning and the design of underlying hardware that can lead to quantum improvements in energy efficiency with good accuracy.

Bio: Kaushik Roy is the Edward G. Tiedemann Jr. Distinguished Professor at Purdue University. He received a BTech degree in electronics and electrical communications engineering from the Indian Institute of Technology, Kharagpur, India, and a PhD degree from the electrical and computer engineering department of the University of Illinois at Urbana-Champaign.