Machine Learning Computer Architecture Enhancements

Please join use for our November meeting, which will be held via Zoom webinar. To attend, you'll need to register at https://acm-org.zoom.us/webinar/register/4116352009786/WN_x1ql3vJkRcy1pf35GcRzqg

This talk will provide an overview of current trends in machine learning computer design concepts with an emphasize on challenges and opportunities facing this field. Some of the traditional computer design ideas that are now being applied to machine learning will be presented. Deep learning has emerged as one of the most promising research fields in artificial intelligence. The significant advancements that deep learning methods have brought about for large scale image classification tasks have generated a surge of excitement in applying the techniques to other problems in computer vision and more broadly into other disciplines of computer science. The phenomenal growth of machine learning is positioned to impact our lives in a way that we have not been able to fully imagine in the near future. This talk will also cover some of the key areas that AI has the potential to revolutionize.

Speaker Bio:

Nader Bagherzadeh is a professor of computer engineering in the department of electrical engineering and computer science at the University of California, Irvine, where he served as a chair from 1998 to 2003. Dr. Bagherzadeh has been involved in research and development in the areas of computer architecture, reconfigurable computing, VLSI chip design, Network-on-Chip, 3D chips, machine learning accelerators, computer graphics, memory, and embedded systems, since he received a Ph.D. degree from the University of Texas at Austin in 1987. He is a Fellow of the IEEE.

Co-sponsors

This event is co-sponsored by the IEEE Orange County Computer Society and the Los Angeles Chapter of the ACM.