Mohsen Imadi; Department of Computer Science and Engineering; University of California, San Diego Towards Learning with Brain Efficiency 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 several publications at top tier conferences and journals. His contributions resulted in over $40M grants funded from... https://calendar.colorado.edu/event/stats_optimization_and_machine_learn...