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DTSTAMP:20230124T171521Z
LOCATION:C155
DTSTART;TZID=America/Chicago:20221114T090000
DTEND;TZID=America/Chicago:20221114T093000
UID:submissions.supercomputing.org_SC22_sess453_ws_pmbsf120@linklings.com
SUMMARY:A Comprehensive Evaluation of Novel AI Accelerators for Deep Learn
 ing Workloads
DESCRIPTION:Workshop\n\nA Comprehensive Evaluation of Novel AI Accelerator
 s for Deep Learning Workloads\n\nEmani, Xie, Raskar, Sastry, Arnold...\n\n
 Scientific applications are increasingly adopting Artificial Intelligence 
 (AI) techniques to advance science. High-performance computing centers are
  evaluating emerging novel hardware accelerators to efficiently run AI-dri
 ven science applications. With a wide diversity in the hardware architectu
 res and software stacks of these systems, it is challenging to understand 
 how these accelerators perform. The state-of-the-art in the evaluation of 
 deep learning workloads primarily focuses on CPUs and GPUs. In this paper,
  we present an overview of dataflow-based novel AI accelerators from Samba
 Nova, Cerebras, Graphcore, and Groq.\n\nWe present a first-of-a-kind evalu
 ation of these accelerators with a diverse set of workloads, such as deep 
 learning (DL) primitives, benchmark models, and scientific machine learnin
 g applications.  We also evaluate the performance of collective communicat
 ion, which is key for distributed DL implementation, along with a study of
  scaling efficiency.  We then discuss key insights, challenges, and opport
 unities in integrating these novel AI accelerators in supercomputing syste
 ms.\n\nSession Format: Recorded\n\nTag: Applications, Architectures, Bench
 marking, Exascale Computing, Modeling and Simulation, Performance, Perform
 ance Portability\n\nRegistration Category: Workshop Reg Pass
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