Abstract: The rise of artificial intelligence (AI) and machine learning applications in computing, the end of Dennard scaling, and the looming end of Moore's law have driven the computing community to investigate new types of computer hardware targeted specifically at AI and machine learning computation. Two new types of computers in this class of hardware, neural accelerators, and neuromorphic computers, have seen a rise in popularity in recent years. Neural accelerators like Google's TPU have focused primarily on accelerating today's deep learning computation, while neuromorphic computers like Intel's Loihi take a more brain-inspired approach and look to the future of AI and machine learning computation. These new types of computer hardware offer significant advantages over traditional computing approaches, including accelerated neural network-style computation and significantly more energy efficiency. This talk will introduce the fundamental computing concepts of these two new types of computer hardware, highlight some of the initial performance results of these systems, and discuss how each type of system will fit into the future landscape of HPC.
Speaker’s Bio: Bio: Catherine (Katie) Schuman is a research scientist at Oak Ridge National Laboratory (ORNL). She received her Ph.D. in Computer Science from the University of Tennessee in 2015, where she completed her dissertation on the use of evolutionary algorithms to train spiking neural networks for neuromorphic systems. She is continuing her study of models and algorithms for neuromorphic computing at ORNL. Katie has an adjunct faculty appointment with the Department of Electrical Engineering and Computer Science at the University of Tennessee, where she co-leads the TENNLab neuromorphic computing research group. Katie has over 70 publications as well as seven patents in the field of neuromorphic computing. Katie received the U.S. Department of Energy Early Career Award in 2019
Last Updated: July 2, 2021 - 8:17 am