MACHINE-LEARNING BASED DATAPATH EXTRACTION

摘要

A datapath extraction tool uses machine-learning models to selectively classify clusters of cells in an integrated circuit design as either datapath logic or non-datapath logic based on cluster features. A support vector machine and a neural network can be used to build compact and run-time efficient models. A cluster is classified as datapath if both the support vector machine and the neural network indicate that it is datapath-like. The cluster features may include automorphism generators for the cell clusters, or physical information based on the cell locations from a previous (e.g., global) placement, such as a ratio of a total cell area for a given cluster to a half-perimeter of a bounding box for the given cluster.

主权项

1. A computer-implemented method of extracting datapath logic from an integrated circuit design, comprising:
receiving a circuit description for the integrated circuit design which includes a plurality of cells interconnected to form a plurality of nets, by executing first instructions in a computer system; generating cell clusters from the circuit description, by executing second instructions in the computer system; evaluating the cell clusters to identify one or more cluster features in the cell clusters, by executing third instructions in the computer system; and selectively classifying the cell clusters as either datapath logic or non-datapath logic using one or more machine-learning models based on the one or more cluster features, by executing fourth instructions in the computer system.