Web: http://arxiv.org/abs/2201.08455

Jan. 24, 2022, 2:10 a.m. | Nan Wu, Jiwon Lee, Yuan Xie, Cong Hao

cs.LG updates on arXiv.org arxiv.org

Despite the stride made by machine learning (ML) based performance modeling,
two major concerns that may impede production-ready ML applications in EDA are
stringent accuracy requirements and generalization capability. To this end, we
propose hybrid graph neural network (GNN) based approaches towards highly
accurate quality-of-result (QoR) estimations with great generalization
capability, specifically targeting logic synthesis optimization. The key idea
is to simultaneously leverage spatio-temporal information from hardware designs
and logic synthesis flows to forecast performance (i.e., delay/area) of various
synthesis …

arxiv graph hybrid information models optimization

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