Efficient Layout Hotspot Detection via Binarized Residual Neural Network
Refereed conference paper presented and published in conference proceedings

香港中文大學研究人員
替代計量分析
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其它資訊
摘要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.
著者Yiyang Jiang, Fan Yang, Hengliang Zhu, Bei Yu, Dian Zhou, Xuan Zeng
會議名稱56th ACM/EDAC/IEEE Design Automation Conference (DAC)
會議開始日02.06.2019
會議完結日06.06.2019
會議地點Las Vegas, NV
會議國家/地區美國
會議論文集題名DAC '19 Proceedings of the 56th Annual Design Automation Conference 2019
出版年份2019
國際標準書號978-1-4503-6725-7
語言美式英語

上次更新時間 2020-18-01 於 03:16