Fetching the paper…
Reading the bibliography…
There is substantial interest in the use of machine learning (ML) based techniques throughout the electronic computer-aided design (CAD) flow, particularly those based on deep learning.
GDSII Stream Format Manual, Release 6.0
Calma Company. 1987 · 1987
Earlier work this paper cites.
Learning from imbalanced data
Haibo He and Edwardo A Garcia. 2008 · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky. 2009 · 2009
Earlier work this paper cites.
Rectified Linear Units Improve Restricted Boltzmann Machines. In International Conference on International Conference on Machine Learning - ICML ’10 . Omnipress, USA, 807–814
Vinod Nair and Geoffrey E. Hinton. 2010 · 2010
Earlier work this paper cites.
ICCAD-2012 CAD contest in fuzzy pattern matching for physical verification and benchmark suite. In 2012 IEEE/ACM International Conference on Computer-Aided Design - ICCAD ’12 . 349–350
J. Andres Torres. 2012 · 2012
Earlier work this paper cites.
Yen-Ting Yu, Ya-Chung Chan, Subarna Sinha, Iris Hui-Ru Jiang, and Charles Chiang. 2012 · 2012
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus. 2014 · 2014
Earlier work this paper cites.
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. 2015 · 2015
Earlier work this paper cites.
A new lithography hotspot detection framework based on AdaBoost classifier and simplified feature extraction. In Design-Process-Technology Co-optimization for Manufacturability IX , Vol. 9427. International Society for Optics and Photonics, 94270S
Tetsuaki Matsunawa, Jhih-Rong Gao, Bei Yu, and David Z Pan. 2015 · 2015
Earlier work this paper cites.
Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio. 2016 · 2016
Earlier work this paper cites.
Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks. In 2016 IEEE Symposium on Security and Privacy - SP ’16 . 582–597
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami. 2016 · 2016
Earlier work this paper cites.
Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition. In ACM SIGSAC Conference on Computer and Communications Security . ACM, Vienna, Austria, 1528–1540
Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer, and Michael K. Reiter. 2016 · 2016
Earlier work this paper cites.
Delving into Transferable Adversarial Examples and Black-box Attacks. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings . OpenReview.net
Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Song. 2017 · 2017
Earlier work this paper cites.
Towards Deep Learning Models Resistant to Adversarial Attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2017 · 2017
Earlier work this paper cites.
Magnet: a two-pronged defense against adversarial examples. In ACM SIGSAC Conference on Computer and Communications Security . ACM, Dallas, TX, 135–147
Dongyu Meng and Hao Chen. 2017 · 2017
Earlier work this paper cites.
On Detecting Adversarial Perturbations. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings . OpenReview.net
Jan Hendrik Metzen, Tim Genewein, Volker Fischer, and Bastian Bischoff. 2017 · 2017
Earlier work this paper cites.
Methods for interpreting and understanding deep neural networks
Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller. 2018 · 2017
Earlier work this paper cites.
Universal Adversarial Perturbations. In 2017 IEEE Conference on Computer Vision and Pattern Recognition - CVPR ’17 . IEEE Computer Society, 86–94
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard. 2017 · 2017
Cited alongside, same era.
Practical black-box attacks against machine learning. In ACM Asia Conference on Computer and Communications Security - ASIACCS ’17 . ACM, Abu Dhabi, United Arab Emirates, 506–519
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami. 2017 · 2017
Cited alongside, same era.
Layout Hotspot Detection with Feature Tensor Generation and Deep Biased Learning. In Design Automation Conference 2017 - DAC ’17 . ACM, Austin, TX, USA, 1–6
Haoyu Yang, Jing Su, Yi Zou, Bei Yu, and Evangeline F. Y. Young. 2017 · 2017
Cited alongside, same era.
Wild patterns: Ten years after the rise of adversarial machine learning
Battista Biggio and Fabio Roli. 2018 · 2018
Cited alongside, same era.
Deep Learning for Logic Optimization Algorithms. In 2018 IEEE International Symposium on Circuits and Systems - ISCAS ’18 . 1–4
Winston Haaswijk, Edo Collins, Benoit Seguin, Mathias Soeken, Frédéric Kaplan, Sabine Süsstrunk, and Giovanni De Micheli. 2018 · 2018
Later among the works it cites.
RouteNet: Routability Prediction for Mixed-size Designs Using Convolutional Neural Network. In International Conference on Computer-Aided Design - ICCAD ’18 . ACM, New York, NY, USA, 80:1–80:8
Zhiyao Xie, Yu-Hung Huang, Guan-Qi Fang, Haoxing Ren, Shao-Yun Fang, Yiran Chen, and Nvidia Corporation. 2018 · 2018
Later among the works it cites.
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks. In Network and Distributed System Security Symposium - NDSS ’18 . The Internet Society, San Diego, California
Weilin Xu, David Evans, and Yanjun Qi. 2018 · 2018
Later among the works it cites.
GAN-OPC: Mask Optimization with Lithography-guided Generative Adversarial Nets. In Design Automation Conference - DAC ’18 . IEEE, 1–6
Haoyu Yang, Shuhe Li, Yuzhe Ma, Bei Yu, and Evangeline F. Y. Young. 2018a · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Guneet S. Dhillon, Kamyar Azizzadenesheli, Jeremy D. Bernstein, Jean Kossaifi, Aran Khanna, Zachary C. Lipton, and Animashree Anandkumar. 2018 · 2018
Cited alongside, same era.
A Machine Learning Approach for Area Prediction of Hardware Designs from Abstract Specifications. In 2018 21st Euromicro Conference on Digital System Design - DSD ’18 . IEEE Computer Society, 413–420
Elena Zennaro, Lorenzo Servadei, Keerthikumara Devarajegowda, and Wolfgang Ecker. 2018 · 2018
Cited alongside, same era.
Robust Physical-World Attacks on Deep Learning Visual Classification. In The IEEE Conference on Computer Vision and Pattern Recognition - CVPR ’18 . IEEE Computer Society, Salt Lake City, Utah, 1625–1634
Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song. 2018 · 2018
Cited alongside, same era.
Machine learning for performance and power modeling of heterogeneous systems. In International Conference on Computer-Aided Design - ICCAD ’18 . ACM, San Diego, California, 1–6
Joseph L. Greathouse and Gabriel H. Loh. 2018 · 2018
Cited alongside, same era.
Countering Adversarial Images using Input Transformations. In 6th International Conference on Learning Representations - ICLR ’18 . OpenReview.net, Vancouver, BC, Canada
Chuan Guo, Mayank Rana, Moustapha Cissé, and Laurens van der Maaten. 2018 · 2018
Cited alongside, same era.
Machine Learning Applications in Physical Design: Recent Results and Directions. In International Symposium on Physical Design - ISPD ’18 . ACM, Monterey, California, USA, 68–73
Andrew B. Kahng. 2018 · 2018
Cited alongside, same era.
Data Efficient Lithography Modeling with Residual Neural Networks and Transfer Learning. In International Symposium on Physical Design - ISPD ’18 . ACM, Monterey, California, USA, 82–89
Yibo Lin, Yuki Watanabe, Taiki Kimura, Tetsuaki Matsunawa, Shigeki Nojima, Meng Li, and David Z. Pan. 2018 · 2018
Cited alongside, same era.
Trojaning Attack on Neural Networks. In Network and Distributed System Security Symposium - NDSS’18 . The Internet Society
Yingqi Liu, Shiqing Ma, Yousra Aafer, Wen-Chuan Lee, Juan Zhai, Weihang Wang, and Xiangyu Zhang. 2018b · 2018
Cited alongside, same era.
Layout Hotspot Detection with Feature Tensor Generation and Deep Biased Learning
Haoyu Yang, Jing Su, Yi Zou, Yuzhe Ma, Bei Yu, and Evangeline F. Y. Young. 2018b · 2018
Later among the works it cites.
Developing synthesis flows without human knowledge. In Design Automation Conference - DAC ’18 . ACM, San Francisco, California, 1–6
Cunxi Yu, Houping Xiao, and Giovanni De Micheli. 2018 · 2018
Later among the works it cites.
GAN-SRAF: Sub-Resolution Assist Feature Generation Using Conditional Generative Adversarial Networks. In Design Automation Conference - DAC ’19 . ACM, Las Vegas, NV, USA, 1–6
Mohamed Baker Alawieh, Yibo Lin, Zaiwei Zhang, Meng Li, Qixing Huang, and David Z. Pan. 2019 · 2019
Closest in time.
CAD-Base: An Attack Vector into the Electronics Supply Chain
Kanad Basu, Samah Mohamed Saeed, Christian Pilato, Mohammed Ashraf, Mohammed Thari Nabeel, Krishnendu Chakrabarty, and Ramesh Karri. 2019 · 2019
Closest in time.
Stealthy Porn: Understanding Real-World Adversarial Images for Illicit Online Promotion. In IEEE Symposium on Security and Privacy - SP ’19 , Vol. 1. IEEE Computer Society, Los Alamitos, CA, USA, 547–561
Bolun Wang, Yuanshun Yao, Shawn Shan, Huiying Li, Bimal Viswanath, Haitao Zhen, and Ben Y. Zhao. 2019 · 2019
Closest in time.
Learning-based prediction of package power delivery network quality. In Asia and South Pacific Design Automation Conference - ASP-DAC ’19 . ACM, Tokyo, Japan, 160–166
Yi Cao, Andrew B. Kahng, Joseph Li, Abinash Roy, Vaishnav Srinivas, and Bangqi Xu. 2019 · 2019
Closest in time.
Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering. In Proceedings of the AAAI Workshop on Artificial Intelligence Safety - SafeAI 2019 . AAAI, 8
Bryant Chen, Wilka Carvalho, Nathalie Baracaldo, Benjamin Edwards, Taesung Lee, Heiko Ludwig, Ian Molloy, and Biplav Srivastava. 2019a · 2019
Closest in time.
SRAF Insertion via Supervised Dictionary Learning. In Asia and South Pacific Design Automation Conference - ASP-DAC ’19 . ACM, New York, NY, USA, 406–411
Hao Geng, Haoyu Yang, Yuzhe Ma, Joydeep Mitra, and Bei Yu. 2019 · 2019
Closest in time.
Calibre LFD
Mentor Graphics. 2019 · 2019
Closest in time.
Efficient Layout Hotspot Detection via Binarized Residual Neural Network. In Design Automation Conference 2019 - DAC ’19 . ACM, Las Vegas, NV, USA, 1–6
Yiyang Jiang, Fan Yang, Hengliang Zhu, Bei Yu, Dian Zhou, and Xuan Zeng. 2019 · 2019
Closest in time.
BadNets: Evaluating Backdooring Attacks on Deep Neural Networks
Tianyu Gu, Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg. 2019 · 2019
Closest in time.
Hotspot detection using squish-net. In Design-Process-Technology Co-optimization for Manufacturability XIII , Vol. 10962. International Society for Optics and Photonics, 109620S
Haoyu Yang, Piyush Pathak, Frank Gennari, Ya-Chieh Lai, and Bei Yu. 2019 · 2019
Closest in time.
Deep learning-based framework for comprehensive mask optimization. In Asia and South Pacific Design Automation Conference - ASP-DAC ’19 . ACM, Tokyo, Japan, 311–316
Bo-Yi Yu, Yong Zhong, Shao-Yun Fang, and Hung-Fei Kuo. 2019 · 2019
Closest in time.