Deep learning (DL) heavily relies on fast hardware and parallel algorithms to train complex neural networks. This BoF will bring together researchers and developers working on the design of next-generation computer systems for DL and parallel DL algorithms that can exploit the potential of these new systems. Research teams working on major deep learning systems deployed in the field will be invited to discuss latest hardware and software trends and to exchange views on the role Artificial Intelligence (AI) in general and DL in particular will play in the near future for big data analytics and HPC applications.
The BoF is co-organized by Volodymyr Kindratenko from NCSA, Yangang Wang from Computer Network Information Center (CNIC) of Chinese Academy of Sciences, and Morris Riedel from Forschungszentrum Juelich.
- Erik Deumens, Director, UF Information Technology — Research Computing, University of Florida
- Paola Buitrago, Director, AI and Big Data, Pittsburgh Supercomputing Center
- Kunle Olukotun, Co-Founder and Chief Technologist, SambaNova Systems