Spiking Neural Networks for More Efficient AI Algorithms

Spiking neural networks (SNNs) have received little attention from the AI community, although they compute in a fundamentally different -- and more biologically inspired -- manner than standard artificial neural networks (ANNs). This can be partially explained by the lack of hardware that natively supports SNNs. However, several groups have recently released neuromorphic hardware that supports SNNs. I will describe example SNN applications that my group has built that demonstrates superior performance on neuromorphic hardware, compared to ANNs on ANN accelerators. I will also discuss new algorithms that outperform standard RNNs (including GRUs, LSTMs, etc.) in both spiking and non-spiking applications.

Speaker Bio:
Professor Chris Eliasmith is currently Director of the Centre for Theoretical Neuroscience at the University of Waterloo and holds a Canada Research Chair in Theoretical Neuroscience. He has authored or co-authored two books and over 90 publications in philosophy, psychology, neuroscience, computer science, and engineering. His book, 'How to build a brain' (Oxford, 2013), describes the Semantic Pointer Architecture for constructing large-scale brain models. His team built what is currently the world's largest functional brain model, 'Spaun,' for which he received the coveted NSERC Polanyi Prize. In addition, he is an expert on neuromorphic computation, writing algorithms for, and designing, brain-like hardware. His team has shown state-of-the-art efficiency on neuromorphic platforms for deep learning, adaptive control, and a variety of other applications.

Видео Spiking Neural Networks for More Efficient AI Algorithms канала Waterloo Artificial Intelligence Institute