Late Breaking Results: Fast System Technology Co-Optimization Framework for Emerging Technology Based on Graph Neural Networks

Tianliang Ma, Guangxi Fan, Xuguang Sun, Zhihui Deng, Kain Lu Low, Leilai Shao*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 61st ACM/IEEE Design Automation Conference, DAC 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798400706011
DOIs
Publication statusPublished - 7 Nov 2024
Externally publishedYes
Event61st ACM/IEEE Design Automation Conference, DAC 2024 - San Francisco, United States
Duration: 23 Jun 202427 Jun 2024

Publication series

NameProceedings - Design Automation Conference
ISSN (Print)0738-100X

Conference

Conference61st ACM/IEEE Design Automation Conference, DAC 2024
Country/TerritoryUnited States
CitySan Francisco
Period23/06/2427/06/24

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