This paper proposes a fast system technology co-optimization (STCO) framework that optimizes power, performance, and area (PPA) for next-generation IC design, addressing the challenges and opportunities presented by novel materials and device architectures. We focus on accelerating the technology level of STCO using AI techniques, by employing graph neural network (GNN)-based approaches for both TCAD simulation and cell library characterization, which are interconnected through a unified compact model, collectively achieving over a 100X speedup over traditional methods. These advancements enable comprehensive STCO iterations with runtime speedups ranging from 1.9X to 14.1X and supports both emerging and traditional technologies.
@article{arxiv.2404.06939,
title = {Late Breaking Results: Fast System Technology Co-Optimization Framework for Emerging Technology Based on Graph Neural Networks},
author = {Tianliang Ma and Guangxi Fan and Xuguang Sun and Zhihui Deng and Kainlu Low and Leilai Shao},
journal= {arXiv preprint arXiv:2404.06939},
year = {2024}
}
Comments
This article has been accepted by the 61st Design Automation Conference(DAC)