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UID:submissions.supercomputing.org_SC24_sess758_misc388@linklings.com
SUMMARY:SiPearl’s Rhea processor: high-bandwidth memory, ultimate solution
  for Large Language Models
DESCRIPTION:Craig Prunty (SiPearl)\n\nUntil now, most LLM are managed usin
 g GPU or dedicated accelerators. But, their cost combined to their low ava
 ilability on the market and their level of energy consumption are promptin
 g us to turn to other solutions. In this context, SiPearl’s high-performan
 ce energy-efficient processor with built-in High Bandwidth Memory (HBM), R
 hea, will be the ultimate solution for LLM workloads. The LLM’s workflow c
 an be divided into three steps: 1) sanitizing and extracting features, 2) 
 building/training founding models, and 3) fine-tuning and using models. Wh
 ile collecting, identifying and extracting relevant features from the raw 
 data (1st step) is already done on CPU, the other steps are still performe
 d on GPU.<br /><br />This talk describes why and how other tasks can be ca
 rried out more advantageously on SiPearl’s processor with built-in HBM. It
  covers inference, fine-tuning and training and demonstrates among other t
 hings the resilience of Rhea which is more flexible to model changes than 
 solutions currently in use.\n\nTag: Artificial Intelligence/Machine Learni
 ng, Codesign\n\nRegistration Category: Workshop Reg Pass\n\nSession Chairs
 : John Feo (Pacific Northwest National Laboratory (PNNL)), Jiyuan Zhang (M
 eta), and Amelie Chi Zhou (Hong Kong Baptist University)\n\n
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