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DTSTART;TZID=America/Denver:20231116T133000
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UID:submissions.supercomputing.org_SC23_sess192_pan108@linklings.com
SUMMARY:Scalable and Adaptable Architectures for AI/HPC Advancement
DESCRIPTION:Keren Bergman (Columbia University); John Shalf (Lawrence Berk
 eley National Laboratory (LBNL)); Sadaf Alam (University of Bristol); Vlad
 imir Stojanovic (University of California, Berkeley; Ayar Labs); Sergey Sh
 umaray (Intel Corporation); Larry Dennison (NVIDIA Corporation); and Vivek
  Raghunathan (Xscape Photonics)\n\nAI/Machine Learning usage is exploding 
 in both application and model size. Predictive analytics, physics, modelin
 g, and new use cases for generative AI/ML are increasing model sizes by 10
 x every 18 months. The custom processors and accelerators used for AI/ML r
 equire continually higher I/O bandwidth to address this model growth. Howe
 ver, how does one deploy a high-performance architecture that is scalable 
 and adaptable through time to address this phenomenon? The panel will disc
 uss the architectures, I/O and large-scale system topologies that will be 
 needed to grow well beyond 200 billion parameters. You will gain insights 
 into system concepts, scaled across workload size, that are both cost-effe
 ctive from a new configurability perspective as well as a focus on energy-
 efficiency. Is there a new Billion Parameters per Watt metric? These are t
 he topics the panel will discuss and debate.\n\nTag: Artificial Intelligen
 ce/Machine Learning\n\nRegistration Category: Tech Program Reg Pass\n\nSes
 sion Chair: Keren Bergman (Columbia University, Ayar Labs)\n\n
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