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DTSTART:19700308T020000
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DTSTAMP:20260402T024533Z
LOCATION:3010\, Level 3
DTSTART;TZID=America/Los_Angeles:20250623T170000
DTEND;TZID=America/Los_Angeles:20250623T173000
UID:dac_DAC 2025_sess257_SSSN042@linklings.com
SUMMARY:Enhancing Design Automation with AI and Quantum Algorithms for Chi
 p Design
DESCRIPTION:Dario Thober (Von Braun Labs)\n\nThe demand for rapid, complex
 , and optimized chip designs for various applications requires enhancement
  in design automation. Although AI/ML can automate and optimize chip desig
 n processes, Quantum Algorithms have shown a significant gain in reducing 
 area, power consumption and nodes. Quantum or quantum-inspired algorithms 
 (QIA) can be run either on standard processors or quantum computers, showi
 ng significant gains in optimizing chip's power consumption as compared to
  pure AI/ML-based design approaches, even for optimizations in large solut
 ion spaces (>10500). Research on these techniques is based on the concept 
 of making semiconductor matrix design nodes equivalent to Quantum Hamilton
 ians with high complexity, which are then solved using QIA to search for t
 he lowest possible energy-level states. We use microprocessors designed on
  a 7nm technology node to benchmark AI/ML tools as compared to those aided
  by QIA. The research also demonstrates that GPU designs using QIA can exp
 erience an exponential advantage over AI/ML, as the number of nodes increa
 ses.\n\nTopics: Design\n\nSession Chair: Sumeet Gupta (Purdue University)\
 n\n
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