Deep Learning Opportunities and Limitations

Friday, December 14, 2018
2:30 PM to 3:30 PM
EER 0.806/0.808
Free and open to the public

In this talk, an overview of current trends in machine learning will be discussed with an emphasize on challenges and opportunities related to computer architecture and parallel processing.  Finally, there will be an overview of our current work covering machine learning data flow graph mapping, multiplier optimizations for CNNs, and thermal analysis of 3D ICs that are projected to be the driving force for highly integrated heterogeneous processors of the future.


Nader Bagherzadeh

University of California, Irvine

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, computer graphics, machine learning accelerators, 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