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BEGIN:VEVENT
DTSTAMP:20240626T180033Z
LOCATION:Level 2 Lobby
DTSTART;TZID=America/Los_Angeles:20240626T180000
DTEND;TZID=America/Los_Angeles:20240626T190000
UID:dac_DAC 2024_sess256_LBR102@linklings.com
SUMMARY:Late Breaking Results: Fast System Technology Co-Optimization Fram
 ework for Emerging Technology Based on Graph Neural Networks
DESCRIPTION:Late Breaking Results Poster\n\nTianliang Ma, Guangxi Fan, Xug
 uang Sun, Zhihui Deng, Kain Lu Low, and Leilai Shao (Shanghai Jiao Tong Un
 iversity)\n\nThis paper proposes a fast system technology co-optimization 
 (STCO) framework that optimizes power, performance, and area (PPA) for nex
 t-generation IC design, addressing the challenges and opportunities presen
 ted by novel materials and device architectures. We focus on accelerating 
 the technology level of STCO using AI techniques, by employing graph neura
 l network (GNN)-based approaches for both TCAD simulation and cell library
  characterization, which are interconnected through a unified compact mode
 l, collectively achieving over a 100X speedup over traditional methods. Th
 ese advancements enable comprehensive STCO iterations with runtime speedup
 s ranging from 1.9X to 14.1X and supports both emerging and traditional te
 chnologies.
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