Hybrid Graph Models for Logic Optimization via Spatio-Temporal Information. (arXiv:2201.08455v1 [cs.LG])

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 optimization