Name
Abstract | ||
|---|---|---|
Layout hotspot detection is of great importance in the physical verification flow. Deep neural network models have been applied to hotspot detection and achieved great successes. The layouts can be viewed as binary images. The binarized neural network can thus be suitable for the hotspot detection problem. In this paper we propose a new deep learning architecture based on binarized neural networks (BNNs) to speed up the neural networks in hotspot detection. A new binarized residual neural network is carefully designed for hotspot detection. Experimental results on ICCAD 2012 Contest benchmarks show that our architecture outperforms all previous hotspot detectors in detecting accuracy and has an 8x speedup over the best deep learning-based solution.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3316781.3317811 | Proceedings of the 56th Annual Design Automation Conference 2019 |
Keywords | Field | DocType |
Binarized Neural Network, Deep Neural Network, Hotspot Detection | Pattern recognition,Computer science,Physical verification,Binary image,Real-time computing,Artificial intelligence,Deep learning,Residual neural network,Artificial neural network,Hotspot (Wi-Fi),Detector,Speedup | Conference |
ISBN | Citations | PageRank |
978-1-4503-6725-7 | 2 | 0.39 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yiyang Jiang | 1 | 4 | 1.09 |
| Fan Yang | 2 | 25 | 6.98 |
| Hengliang Zhu | 3 | 85 | 13.49 |
| Bei Yu | 4 | 656 | 74.07 |
| Dian Zhou | 5 | 260 | 56.14 |
| Xuan Zeng | 6 | 408 | 75.96 |