Universal routability prediction method based on few-shot learning

Abstract

The present invention relates to a universal routability prediction method based on few-shot learning and belongs to the technical field of computer-aided design of integrated circuits. A universal routability prediction is converted into a meta learning scenario, and a prediction method based on the few-shot learning (FSL) is provided. The method only needs to provide a feature of a query chip and a label pair example set, to flexibly adapt to a new prediction task without additional training. For a data imbalance problem generally existing in the field of electronic design automation, the present invention further introduces a meta learning policy based on importance sampling for optimizing a training process of a model. To train the provided method, an FSL dataset based on CircuitNet and ISPD2015 datasets is constructed.

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