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DTSTAMP:20260402T024534Z
LOCATION:Engineering Posters\, Level 2 Exhibit Hall
DTSTART;TZID=America/Los_Angeles:20250623T170000
DTEND;TZID=America/Los_Angeles:20250623T180000
UID:dac_DAC 2025_sess263_ENGPOST365@linklings.com
SUMMARY:Optimizing Network Storage for AI-Powered EDA Deployments
DESCRIPTION:Prathna Sekar (Keysight Technologies)\n\nNearly all industries
  are striving to implement AI-powered solutions to significantly enhance p
 erformance across various workflows and functional groups. However, this s
 hift brings a new, emerging issue. AI-driven EDA tools and workflows, whil
 e promising to enhance design processes, will substantially increase data 
 volume and provisioning needs due to their dependence on large datasets fo
 r training and operation. These tools often magnify requirements by severa
 l orders of magnitude. Therefore, an effective method to optimize network 
 storage is essential to manage the data explosion caused by AI-enabled EDA
  workflows.\nIn this proposal, we go over the challenges and demonstrate h
 ow a smart caching agent solution can provide maximum network storage opti
 mization and the best performance when it comes to managing large datasets
  generated through such AI-powered EDA deployments.\n\n
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