Towards Learning with Brain Efficiency

Nov 1, 2019  |  3:00pm | ESB 2001
Mohsen Imani
Ph.D. Candidate in the Department of Computer Science and Engineering at UC San Diego

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.


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 several publications at top tier conferences and journals. His contributions resulted in over $40M grants funded from multiple governmental agencies (4x NSF, 3x SRC) and several companies including IBM, Intel, Micron, and Qualcomm. He has received the most prestigious awards from the UCSD school of engineering including the Gordon Engineering Leadership Award and the Outstanding Graduate Research Award. He also got several nominations for the best paper awards from multiple conferences. Mohsen will be in the academic job market this year. 

HostTim SherwoodEvent TypeSeminar