Towards Learning with Brain Efficiency
Mohsen Imani IPhD Candidate, Department of Computer Science and Engineering, UC San Diego
Faculty Host:Ajay Joshi
Refreshments at 10:45 am
Abstract: Modern computing systems are plagued with significant issues in efficiently performing learning tasks. In this talk, I will present a new brain-inspired computing architecture. It supports a wide range of learning tasks while offering higher system efficiency than the other existing platforms. I will first focus on HyperDimensional (HD) computing, an alternative method of computation which exploits key principles of brain functionality: (i) robustness to noise/error and (ii) intertwined memory and logic. To this end, we design a new learning algorithm resilient to hardware failure. We then build the architecture exploiting emerging technologies to enable processing in memory. I will also show how we use the new architecture to accelerate other brain-like computations such as deep learning and other big data processing.
Bio: Mohsen Imani is a Ph.D. candidate in the Department of Computer Science and Engineering at UC San Diego. His research interests are in brain-inspired computing and computer architecture. He is an author of more than 85 publications at top tier conferences and journals. He has received several awards during his study including the outstanding graduate student at CS department, outstanding research award and the best leadership award in the school of engineering at UCSD.