Brain-inspired AI: from algorithms to hardware platforms

The escalating complexity of AI models has resulted in soaring energy expenses for training and inference. These models also frequently yield unreliable outcomes, lacking the ability to address uncertainty in both models and data. Addressing these issues requires breakthroughs in algorithms, architectures, and fundamental computational units.

In this talk, Professor Bipin Rajendran will outline recent progress in creating efficient and reliable AI drawing inspiration from the computational architecture of the brain. He will describe their approach for developing brain-inspired algorithms, and in-memory computing architectures that employ nanoscale devices to implement deterministic as well as probabilistic AI models, opening up a path to reliable and efficient AI hardware.

This seminar is part of the King’s Institute for Artificial Intelligence’s seminar series.

Professor Bipin Rajendran is a Professor of Intelligent Computing Systems in the Department of Engineering, King's College London, where he directs the King's Laboratory for Intelligent Computing. He also co-leads the Centre for Intelligent Information Processing (CIIPS).

Host or Publisher King’s Institute for Artificial Intelligence