On Predicting Solution Quality of Maze Routing Using Convolutional Neural Network

Routing is a crucial step for modern VLSI designs, and a typical router often uses a maze routing algorithm to re-route each congested net iteratively until the solution converges. The new path found by the router at each iteration in general will be discarded if it does not have a lower routing cos...

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Bibliographic Details
Main Authors: Chang, Kuei-Huan, Pan, Hsin-Hung, Wang, Ting-Chi, Chen, Po-Yuan, Shen, Chin-Fang Cindy
Format: Conference Proceeding
Language:English
Subjects:
C++ languages
Convolutional neural networks
Costs
Prediction algorithms
Predictive models
Routing
Very large scale integration
Online Access:Request full text
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Summary:Routing is a crucial step for modern VLSI designs, and a typical router often uses a maze routing algorithm to re-route each congested net iteratively until the solution converges. The new path found by the router at each iteration in general will be discarded if it does not have a lower routing cost than the current one. In this paper, we aim to predict whether the path generated by a maze router has a routing cost less than a given bound. This prediction problem is transformed into a binary classification problem for which a convolutional neural network (CNN) is trained. We extracted the routing results of an academic global router from more than a dozen circuits, and used them to train and test our CNN model. The experiments show that our CNN model can reach 78.2% prediction accuracy while the prediction is more than two times faster than maze routing on average.
ISSN:1948-3295
DOI:10.1109/ISQED54688.2022.9806227