Abstract
The convergence of AI and HPC has created a fertile venue that is ripe for imaginative researchers — versed in AI technology — to make a big impact in a variety of scientific fields. From new hardware to new computational approaches, the true impact of deep- and machine learning on HPC is, in a word, “everywhere”.
Just as technology changes in the personal computer market brought about a revolution in the design and implementation of the systems and algorithms used in high performance computing (HPC), so are recent technology changes in machine learning bringing about an AI revolution in the HPC community.
Expect new HPC analytic techniques including the use of GANs (Generative Adversarial Networks) in physics-based modeling and simulation, as well as reduced precision math libraries such as NLAFET and HiCMA to revolutionise many fields of research.
Other benefits of the convergence of AI and HPC include the physical instantiation of data flow architectures in FPGAs and ASICs, plus the development of powerful data analytic services.
Speaker bio:
Rob Farber was a pioneer in the field of neural networks while on staff as a scientist in the Theoretical Division at Los Alamos National Laboratory.
He is active in the field and works with companies and national laboratories as a consultant, plus teaches about HPC and AI technology worldwide.
Rob’s resume includes research positions at NERSC, PNNL, the Santa Fe Institute, and The Center for High-end Computing in Dublin, Ireland.
Rob also co-founded two companies that achieved liquidity events (along with a few that didn’t). The first company manufactured and marketed one of the first virtual memory microcomputers and the second combined HPC and machine learning to facilitate the search for drug candidates.
Rob can be reached at: info@techenablement.com.
More information | Presentation slides (PDF 9 MB)
