Efficient Layout Hotspot Detection via Binarized Residual Neural Network Ensemble

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 (BNN) can thus be suitable for the hotspot detection problem. In this article, we propose a new deep learning architecture based on 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 and 2019 benchmarks show that our architecture outperforms previous hotspot detectors in detecting accuracy and has an 8x speedup over the best deep learning-based solution. Since the BNN-based model is quite computationally efficient, a good tradeoff can be achieved between the efficiency and performance of the hotspot detector by applying ensemble learning approaches. Experimental results show that the ensemble models achieve better hotspot detection performance than the original with acceptable speed loss.

Automated summary

Layout hotspot detection is crucial in the physical verification flow, and deep neural network models have shown great success in this area. This article introduces a new deep learning architecture based on binarized neural networks for speeding up hotspot detection. Experimental results demonstrate that the proposed architecture outperforms previous hotspot detectors in accuracy and is 8 times faster than the best deep learning-based solution.