Presentation
Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction
SessionSmart Circuits, Smarter Algorithms: AI-Driven Innovations in Circuit Modeling and Optimization
DescriptionGraph representation learning is a powerful approach to extract features from graph-structured data such as analog/mixed-signal (AMS) circuits. However, the training of deep learning models for AMS design is severely limited by the scarcity of integrated circuit design data. In this work, we present CirGPS, a few-shot learning method for parasitic effect prediction in AMS circuits. The circuit netlist is modeled as a heterogeneous graph while the coupling capacitance is modeled as a link. CirGPS is pre-trained on link prediction and fine-tuned on edge regression. The proposed method starts with a small-hop sampling technique that converts a link or a node into a subgraph. Then, the subgraph embeddings are learned with a hybrid graph Transformer. Additionally, CirGPS integrates a low-cost positional encoding that summarizes the positional and structural information of the target link. CirGPS improves the accuracy of coupling existence by at least 20% and reduces the MAE of capacitance estimation by at least 0.067 compared to existing methods. Our method naturally has good scalability and can be applied with zero-shot learning to a wide variety of AMS designs. Through our ablation studies, we provide valuable insights into graph models for representation learning.
Event Type
Research Manuscript
TimeWednesday, June 2510:30am - 10:45am PDT
Location3000, Level 3
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