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RouteNet: Routability prediction for mixed-size designs using convolutional neural network

Publication ,  Conference
Xie, Z; Huang, YH; Fang, GQ; Ren, H; Fang, SY; Chen, Y; Hu, J
Published in: IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
November 5, 2018

Early routability prediction helps designers and tools perform preventive measures so that design rule violations can be avoided in a proactive manner. However, it is a huge challenge to have a predictor that is both accurate and fast. In this work, we study how to leverage convolutional neural network to address this challenge. The proposed method, called RouteNet, can either evaluate the overall routability of cell placement solutions without global routing or predict the locations of DRC (Design Rule Checking) hotspots. In both cases, large macros in mixed-size designs are taken into consideration. Experiments on benchmark circuits show that RouteNet can forecast overall routability with accuracy similar to that of global router while using substantially less runtime. For DRC hotspot prediction, RouteNet improves accuracy by 50% compared to global routing. It also significantly outperforms other machine learning approaches such as support vector machine and logistic regression.

Duke Scholars

Published In

IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD

DOI

ISSN

1092-3152

Publication Date

November 5, 2018
 

Citation

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Xie, Z., Huang, Y. H., Fang, G. Q., Ren, H., Fang, S. Y., Chen, Y., & Hu, J. (2018). RouteNet: Routability prediction for mixed-size designs using convolutional neural network. In IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD. https://doi.org/10.1145/3240765.3240843
Xie, Z., Y. H. Huang, G. Q. Fang, H. Ren, S. Y. Fang, Y. Chen, and J. Hu. “RouteNet: Routability prediction for mixed-size designs using convolutional neural network.” In IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD, 2018. https://doi.org/10.1145/3240765.3240843.
Xie Z, Huang YH, Fang GQ, Ren H, Fang SY, Chen Y, et al. RouteNet: Routability prediction for mixed-size designs using convolutional neural network. In: IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD. 2018.
Xie, Z., et al. “RouteNet: Routability prediction for mixed-size designs using convolutional neural network.” IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD, 2018. Scopus, doi:10.1145/3240765.3240843.
Xie Z, Huang YH, Fang GQ, Ren H, Fang SY, Chen Y, Hu J. RouteNet: Routability prediction for mixed-size designs using convolutional neural network. IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD. 2018.

Published In

IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD

DOI

ISSN

1092-3152

Publication Date

November 5, 2018