Publications

Conference Papers (Dr. Lin’s students; * equal contribution)

  1. [DAC’21 Workshop] Zheng Zhan, Geng Yuan, Yifan Gong, Yushu Wu, Pu Zhao, Wei Niu, Bin Ren, Xue Lin, and Yanzhi Wang. Achieving real-time super-resolution application on mobile phone. In the ROAD4NN 2021 Workshop at DAC, 2021.
  2. [2021 VNN-COMP] [Top Highest Score Award] Huan Zhang*, Kaidi Xu*, Shiqi Wang*, Zhouxing Shi, Yifan Wang, Xue Lin, Suman Jana, Cho-Jui Hsieh,  and Zico Kolter. Alpha-Beta-CROWN. Verification of Neural Networks Competition Top Highest Score Award and Verification of Neural Networks Competition Category Winner for Verivital, eran, cifar 10_resnet, nn4sys, marabou-cifar 10 in 2021 VNN-COMP co-located with the 33rd International’s Conference on Computer-Aided Verification (CAV’21). vnncomp2021_certificate_α-β-crown
  3. [ICCV’21] Zheng Zhan, Yifan Gong, Pu Zhao, Geng Yuan, Wei Niu, Yushu Wu, Tianyu Zhang, Malith Jayaweera, David Kaeli, Bin Ren, Xue Lin, and Yanzhi Wang. Achieving on-mobile real-time super-resolution with neural architecture and pruning search. In Proceedings of the IEEE International Conference on Computer Vision, 2021. Acceptance rate: 25.9% (1617/6236)
  4. [ICCV’21] Sung-En Chang*, Yanyu Li*, Mengshu Sun*, Weiwen Jiang, Sijia Liu, Yanzhi Wang, and Xue Lin. RMSMP: A novel deep neural network quantization framework with row-wise mixed schemes and multiple precisions. In Proceedings of the IEEE International Conference on Computer Vision, 2021. Acceptance rate: 25.9% (1617/6236) (NSF SHF project)
  5. [ICML’21 Workshop] Shiqi Wang, Huan Zhang, Kaidi Xu, Xue Lin, Suman Jana, Cho-Jui Hsieh, J Zico Kolter. Beta-crown: efficient bound propagation with per-neuron split constraints for neural network robustness verification. In ICML 2021 Workshop on Adversarial Machine Learning.
  6. [AdvML’21] Yize Li, Pu Zhao, Yuguang Yao, Vishal Asnani, Yifan Gong, Yimeng Zhang, Zhengang Li, Xiaoming Liu, Sijia Liu, and Xue Lin. Supervised classification on deep neural network attack toolchains. In the 3rd Workshop on Adversarial Learning Methods for Machine Learning and Data Mining at KDD, 2021. (DARPA RED project)
  7. [AdvML’21] Chenan Wang, Pu Zhao, Siyue Wang, and Xue Lin. Detection and recovery against deep neural network fault injection attacks based on contrastive learning. In the 3rd Workshop on Adversarial Learning Methods for Machine Learning and Data Mining at KDD, 2021. (NSF SaTC project)
  8. [USENIX Security ’21 Winter] Takami Sato, Junjie Shen, Ningfei Wang, Yunhan Jia, Xue Lin, and Qi Alfred Chen. Security of deep learning based automated lane centering under physical-world adversarial attack. In Proceedings of the 30th USENIX Security Symposium, 2021. Acceptance rate: xx.x% (xxx/xxx) (NSF CPS project)
  9. [IJCAI’21] Siyue Wang, Xiao Wang, Pin-yu Chen, Pu Zhao, and Xue Lin. Characteristic examples: high-robustness, low-transferability fingerprinting of neural networks. In Proceedings of the 30th International Joint Conference on Artificial Intelligence, 2021. Acceptance rate: 13.9% (587/4204) (NSF SaTC project)
  10. [NEHWS’21] Chenan Wang, Siyue Wang, Pu Zhao, Yunsi Fei, and Xue Lin. Detection and recovery against deep neural network fault injection attacks based on contrastive learning. Presented at New England Hardware Security Day, April 2021. (NSF SaTC project)
  11. [ICLR’21 Workshop] Siyue Wang, Xiao Wang, Pin-Yu Chen, Pu Zhao, and Xue Lin. High-robustness, low-transferability fingerprinting of neural networks. In International Conference on Learning Representations Workshop on Security and Safety in Machine Learning Systems, 2021. (NSF SaTC project)
  12. [ICLR’21 Workshop][Best Paper Award] Xiaolong Ma, Zhengang Li, Geng Yuan, Wei Niu Bin Ren, Yanzhi Wang, and Xue Lin. Memory-bounded sparse training on the edge. In International Conference on Learning Representations Workshop on Hardware Aware Efficient Training, 2021.
  13. [ICLR’21 Workshop] Hao Cheng, Kaidi Xu, Chenan Wang, Xue Lin, Bhavya Kailkhura, and Ryan Goldhahn. Mixture of robust experts (MoRE): a flexible defense against multiple perturbations. In International Conference on Learning Representations Workshop on Robust and Reliable Machine Learning in the Real World, 2021. (LLNL project)
  14. [RTAS’21 Brief Presentation] Geng Yuan, Peiyang Dong, Mengshu Sun, Wei Niu, Zhengang Li, Yuxuan Cai, Jun Liu, Weiwen Jiang, Xue Lin, Bin Ren, Xulong Tang, and Yanzhi Wang. Work in Progress: Mobile or FPGA? a comprehensive evaluation on energy efficiency and a unified optimization framework. In Proceedings of the 27th IEEE Real-Time and Embedded Technology and Applications Symposium, 2021. (NSF AitF project)
  15. [RTAS’21 Brief Presentation] Pu Zhao, Wei Niu, Geng Yuan, Yuxuan Cai, Hsin-Hsuan Sung, Shaoshan Liu, Sijia Liu, Xipeng Shen, Bin Ren, Yanzhi Wang, and Xue Lin. Brief Industry Paper: Towards real-time 3D object detection for autonomous vehicles with pruning search. In Proceedings of the 27th IEEE Real-Time and Embedded Technology and Applications Symposium, 2021. (NSF SaTC project)
  16. [CVPR’21 Oral Paper: top 5%] Zhengang Li*, Pu Zhao*, Geng Yuan, Wei Niu, Yanyu Li, Yuxuan Cai, Xuan Shen, Zheng Zhan, Zhenglun Kong, Qing Jin, Zhiyu Chen, Sijia Liu, Kaiyuan Yang, Yanzhi Wang, Bin Ren, and Xue Lin. NPAS: A compiler-aware framework of unified network pruning and architecture search for beyond real-time mobile acceleration. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021. Acceptance rate: 21.2% (1593/7500) (NSF SHF project)
  17. [DAC’21] Pu Zhao, Geng Yuan, Yuxuan Cai, Wei Niu, Qi Liu, Wujie Wen, Yanzhi Wang, Bin Ren, and Xue Lin. Neural pruning search for real-time object detection of autonomous vehicles. In Proceedings of the 58th Annual Design Automation Conference, ACM, 2021. Acceptance rate: 23% (NSF CPS project)
  18. [CogArch’21] Sung-En Chang, Yanyu Li, Mengshu Sun, Xue Lin, and Yanzhi Wang. ILMPQ: An intra-layer multi-precision deep neural network quantization framework for FPGA. Presented at the 5th Workshop on Cognitive Architectures in HPCA, 2021. (NSF SHF project)
  19. [CogArch’21] Pu Zhao, Wei Niu, Geng Yuan, Yuxuan Cai, Bin Ren, Yanzhi Wang, and Xue Lin. Achieving real-time object detection on mobile devices with neural pruning search. Presented at the 5th Workshop on Cognitive Architectures in HPCA, 2021. (NSF CPS project)
  20. [AutoSec’21] Takami Sato, Junjie Shen, Ningfei Wang, Yunhan Jia, Xue Lin, and Qi Alfred Chen. WIP: Deployability improvement, stealthiness user study, and safety impact assessment on real vehicle for dirty road patch attack. Presented at the 3rd International Workshop on Automotive and Autonomous Vehicle Security in NDSS, 2021. (NSF CPS project)
  21. [AutoSec’21] Takami Sato, Junjie Shen, Ningfei Wang, Yunhan Jia, Xue Lin, and Qi Alfred Chen. Demo: Security of deep learning based automated lane centering under physical-world attack. Presented at the 3rd International Workshop on Automotive and Autonomous Vehicle Security in NDSS, 2021. (NSF CPS project)
  22. [ICLR’21] Kaidi Xu, Huan Zhang, Shiqi Wang, Yihan Wang, Suman Jana, Xue Lin, and Cho-Jui Hsieh. Fast and complete: enabling complete neural network verification with rapid and massively parallel incomplete verifiers. In International Conference on Learning Representations, 2021. Acceptance rate: 28.7% (860/2997) (Air Force project)
  23. [AccML’21] Pu Zhao, Geng Yuan, Yuxuan Cai, Wei Niu, Bin Ren, Yanzhi Wang, and Xue Lin. Neural pruning search for real-time object detection of autonomous vehicles. Presented at the 3rd Accelerated Machine Learning Workshop in HiPEAC, 2021. (NSF CPS project)
  24. [AccML’21] Zhengang Li, Geng Yuan, Wei Niu, Yanyu Li, Pu Zhao, Yuxuan Cai, Xuan Shen, Zheng Zhan, Zhenglun Kong, Qing Jin, Bin Ren, Yanzhi Wang, and Xue Lin. NPS: a compiler-aware framework of unified network pruning for beyond real-time mobile acceleration. Presented at the 3rd Accelerated Machine Learning Workshop in HiPEAC, 2021. (NSF AitF project)
  25. [AAAI’21] Wei Niu*, Mengshu Sun*, Zhengang Li*, Jou-An Chen, Jiexiong Guan, Xipeng Shen, Yanzhi Wang, Sijia Liu, Xue Lin, and Bin Ren. RT3D: achieving real-time execution of 3D convolutional neural networks on mobile devices. In Proceedings of the AAAI Conference on Artificial Intelligence, 2021. Acceptance rate: 21% (1692/7911) (NSF CPS project)
  26. [HPCA’21] Sung-En Chang*, Yanyu Li*, Mengshu Sun*, Runbin Shi, Hayden K.-H. So, Yanzhi Wang, Xuehai Qian, and Xue Lin. Mix and match: a novel FPGA-centric deep neural network quantization framework. In Proceedings of the 2021 IEEE International Symposium on High Performance Computer Architecture, 2021. Acceptance rate: 24.4% (63/258) (NSF SHF project)
  27. [ASP-DAC’21] Hongjia Li, Geng Yuan, Wei Niu, Yuxuan Cai, Mengshu Sun, Zhengang Li, Bin Ren, Xue Lin, and Yanzhi Wang. Real-time mobile acceleration of DNNs: from computer vision to medical applications. In Proceedings of the 27th Asia and South Pacific Design Automation Conference, 2021.
  28. [WISE’20][1st Place Winner of Best PresentationSiyue Wang, and Xue Lin. Intrinsic examples: robust fingerprinting of deep neural networks. Presented at the 4th Workshop for Women in Hardware and Systems Security, 2020. (NSF SaTC project)
  29. [NeurIPS’20] Kaidi Xu, Zhouxing Shi, Huan Zhang, Yihan Wang, Kai-Wei Chang, Minlie Huang, Bhavya Kailkhura, Xue Lin, and Cho-Jui Hsieh. Automatic perturbation analysis for scalable certified robustness and beyond. In Proceedings of Advances in Neural Information Processing Systems, 2020. Acceptance rate: 20.1% (1900/9454) (NSF CPS project)
  30. [ISLPED’20][Design Contest 1st Place] Geng Yuan, Wei Niu, Pu Zhao, Xue Lin, Bin Ren, and Yanzhi Wang. CoCoPIE: A framework of compression-compilation co-design towards ultra-high energy efficiency and real-time DNN inference on mobile devices. ACM/IEEE International Symposium on Low Power Electronics and Design, 2020. (NSF AitF project)
  31. [ICCAD’20] Cheng Gongye, Xiang Zhang, Majid Sabbagh, Hongjia Li, Geng Yuan, Xue Lin, Thomas Wahl, and Yunsi Fei. New passive and active attacks on deep neural networks in medical applications. In Proceedings of the International Conference on Computer-Aided Design. ACM, 2020. (NSF SaTC project)
  32. [ECCV’20 Demo] Wei Niu, Mengshu Sun, Zhengang Li, Geng Yuan, Pu Zhao, Xue Lin, and Bin Ren. Real-time 3D CNN Inference for Action Recognition on Mobile Devices. European Conference on Computer Vision, 2020. (NSF AitF project)
  33. [ECCV’20 Demo] Zheng Zhan, Pu Zhao, Geng Yuan, Wei Liu, Bin Ren, and Xue Lin. Real-time DNN inferences on mobile devices for various practical deep learning applications. European Conference on Computer Vision, 2020. (NSF AitF project)
  34. [ECCV’20 Spotlight Paper: top 5%] Kaidi Xu, Gaoyuan Zhang, Sijia Liu, Quanfu Fan, Mengshu Sun, Hongge Chen, Pin-Yu Chen, Yanzhi Wang, and Xue Lin. Adversarial T-shirt! evading person detectors in a physical world. In Proceedings of the European Conference on Computer Vision, 2020. Acceptance rate: 27% (1361/5025) (NSF CPS project)
  35. [GLSVLSI’20 Special Session Paper] Yifan Gong, Zheng Zhan, Zhengang Li, Wei Niu, Bin Ren, Xiaolong Ma, Xiaolin Xu, Caiwen Ding, and Xue Lin. A privacy-preserving-oriented DNN pruning and mobile acceleration framework. In Proceedings of the 2020 on Great Lakes Symposium on VLSI, pages xx–xx. ACM, 2020. (NSF SHF project)
  36. [IJCAI-PRICAI’20 Demonstrations Track] Wei Niu*, Pu Zhao*, Zheng Zhan, Xue Lin, Yanzhi Wang, and Bin Ren. Towards real-time DNN inference on mobile platforms with model pruning and compiler optimization. In Proceedings of the International Joint Conference on Artificial Intelligence-Pacific Rim International Conference on Artificial Intelligence 2020. Acceptance rate: 26.7% (NSF CPS project)
  37. [Reported by News@Northeastern, Boston Globe, The Register, Communications of the ACM, VentureBeat, et al.] Kaidi Xu, Gaoyuan Zhang, Sijia Liu, Quanfu Fan, Mengshu Sun, Hongge Chen, Pin-Yu Chen, Yanzhi Wang, and Xue Lin. Adversarial t-shirt! Evading person detectors in a physical world. arXiv preprint arXiv:1910.11099, 2019.
  38. [NDSS’20][Best Technical Poster Award] Takami Sato, Junjie Shen, Ningfei Wang, Yunhan Jia, Xue Lin, and Qi Chen. Security of deep learning based lane keeping assistance systems under physical-world adversarial attack. Presented at the NDSS Symposium, Feb 23 – 26, 2020 in San Diego, California. (NSF CPS project)
  39. [DAC’20] Mengshu Sun, Pu Zhao, Mehmet Gungor, Miriam Leeser, Massoud Pedram, and Xue Lin. 3D CNN acceleration on FPGA using hardware-aware pruning. In Proceedings of the 57th Annual Design Automation Conference 2020, page XXX. ACM, 2020. Acceptance rate: 23% (NSF SHF project)
  40. [DAC’20] Peiyan Dong, Siyue Wang, Wei Niu, Chengming Zhang, Sheng Lin, Zhengang Li, Yifan Gong, Bin Ren, Xue Lin, and Dingwen Tao. RTMobile: beyond real-time mobile acceleration of RNNs for speech recognition. In Proceedings of the 57th Annual Design Automation Conference 2020, page XXX. ACM, 2020. Acceptance rate: 23% (NSF SHF project)
  41. [ICASSP’20Kaidi Xu, Sijia Liu, Pin-Yu Chen, Mengshu Sun, Caiwen Ding, Bhavya Kailkhura, and Xue Lin. Towards an efficient and general framework of robust training for graph neural networks. In Proceedings of the ICASSP, 2020. (LLNL project)
  42. [ICASSP’20] Xiao Wang, Siyue Wang, Pin-Yu Chen, Xue Lin, and Peter Chin. ADVMS: a multi-source multi-cost defense against adversarial attacks. In Proceedings of the ICASSP, 2020. (Air Force project)
  43. [ASPLOS’20] Wei Niu, Xiaolong Ma, Sheng Lin, Shihao Wang, Xuehai Qian, Xue Lin, Yanzhi Wang, and Bin Ren. PatDNN: achieving real-time DNN execution on mobile devices with pattern-based weight pruning. In Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems. ACM, 2020. Acceptance rate: 21.1% (NSF SHF project)
  44. [ICLR’20Pu Zhao, Pin-Yu Chen, Payel Das, Karthikeyan Natesan Ramamurthy, and Xue Lin. Bridging mode connectivity in loss landscapes and adversarial robustness. In International Conference on Learning Representations, 2020. Acceptance rate: 26.5% (687/2594) (NSF SaTC project)
  45. [AAAI’20Pu Zhao, Pin-Yu Chen, Siyue Wang, and Xue Lin. Towards query-efficient black-box adversary with zeroth-order natural gradient descent. In Proceedings of the AAAI Conference on Artificial Intelligence, 2020. Acceptance rate: 20.6% (1591/7737) (NSF CPS project)
  46. [AAAI’20] Lily Weng*, Pu Zhao*, Sijia Liu, Pin-Yu Chen, Xue Lin, and Luca Daniel. Towards certificated model robustness against weight perturbations. In Proceedings of the AAAI Conference on Artificial Intelligence, 2020. Acceptance rate: 20.6% (1591/7737) (NSF SaTC project)
  47. [AAAI’20] Xiaolong Ma, Fu-ming Guo, Wei Niu, Xue Lin, Jian Tang, Bin Ren, and Yanzhi Wang. PCONV: the missing but desirable sparsity in DNN weight pruning for real-time execution on mobile device. In Proceedings of the AAAI Conference on Artificial Intelligence, 2020. Acceptance rate: 20.6% (1591/7737) (NSF SHF project)
  48. [HOST’20 Tutorial] Xue Lin, Yunsi Fei, and Thomas Wahl. Protecting confidentiality and integrity of deep neural networks. In IEEE International Symposium on Hardware Oriented Security and Trust (HOST), 2020. (NSF SaTC project)
  49. [NeurIPS’19] Xiangyi Chen*, Sijia Liu*, Kaidi Xu*, Xingguo Li, Xue Lin, Mingyi Hong, and David Cox. Zo-adamm: Zeroth-order adaptive momentum method for black-box optimization. In Proceedings of Advances in Neural Information Processing Systems, 2019. Acceptance rate: 21.1% (1428/6743) (NSF CPS project)
  50. [ICCV’19Pu Zhao, Sijia Liu, Pin-Yu Chen, Nghia Hoang, Kaidi Xu, Bhavya Kailkhura, and Xue Lin. On the design of black-box adversarial examples by leveraging gradient-free optimization and operator splitting method. In Proceedings of the IEEE International Conference on Computer Vision, pages 121–130, 2019. Acceptance rate: 25% (1077/4303) (NSF CPS project)
  51. [ICCV’19] Shaokai Ye*, Kaidi Xu*, Sijia Liu, Hao Cheng, Jan-Henrik Lambrechts, Huan Zhang, Aojun Zhou, Kaisheng Ma, Yanzhi Wang, and Xue Lin. Adversarial robustness vs model compression, or both? In Proceedings of the IEEE International Conference on Computer Vision, pages 111–120, 2019. Acceptance rate: 25% (1077/4303) (NSF CPS project)
  52. [IJCAI’19Kaidi Xu, Hongge Chen, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Mingyi Hong, and Xue Lin. Topology attack and defense for graph neural networks: An optimization perspective. In Proceedings of the 28th International Joint Conference on Artificial Intelligence. AAAI Press, 2019. Acceptance rate: 17.8% (850/4752) (Air Force project)
  53. [IJCAI’19] Xiao Wang*, Siyue Wang*, Pin-Yu Chen, Yanzhi Wang, Brian Kulis, Xue Lin, and Peter Chin. Protecting neural networks with hierarchical random switching: towards better robustness-accuracy trade-off for stochastic defenses. In Proceedings of the 28th International Joint Conference on Artificial Intelligence, pages 6013–6019. AAAI Press, 2019. Acceptance rate: 17.8% (850/4752)
  54. [CVPR’19] Zihao Liu, Qi Liu, Tao Liu, Nuo Xu, Xue Lin, Yanzhi Wang, and Wujie Wen. Feature distillation: Dnn-oriented jpeg compression against adversarial examples. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 860–868, 2019. Acceptance rate: 25.2% (1299/5165)
  55. Hao Cheng, Kaidi Xu, Sijia Liu, Pin-Yu Chen, Pu Zhao, and Xue Lin. Defending against Backdoor Attack on Deep Neural Networks. KDD Workshop on Adversarial Learning Methods for Machine Learning and Data Mining (AdvML), 2019.
  56. Xiao Wang, Siyue Wang, Pin-Yu Chen, Xue Lin, and Peter Chin. Block Switching: A Stochastic Approach for Deep Learning Security. KDD Workshop on Adversarial Learning Methods for Machine Learning and Data Mining (AdvML), 2019. (Air Force project)
  57. [GLSVLSI’19Mengshu Sun, Pu Zhao, Yanzhi Wang, Naehyuck Chang, and Xue Lin. Hsim-dnn: Hardware simulator for computation-, storage-and power-efficient deep neural networks. In Proceedings of the 2019 on Great Lakes Symposium on VLSI, pages 81–86. ACM, 2019. Acceptance rate: 29% (NSF AitF project)
  58. [DAC’19Pu Zhao, Siyue Wang, Cheng Gongye, Yanzhi Wang, Yunsi Fei, and Xue Lin. Fault sneaking attack: a stealthy framework for misleading deep neural networks. In Proceedings of the 56th Annual Design Automation Conference 2019, page 165. ACM, 2019. Acceptance rate: 24.8% (202/815) (Air Force project and ONR project)
  59. [ICLR’19Kaidi Xu, Sijia Liu, Pu Zhao, Pin-Yu Chen, Huan Zhang, Quanfu Fan, Deniz Erdogmus, Yanzhi Wang, and Xue Lin. Structured adversarial attack: Towards general implementation and better interpretability. In International Conference on Learning Representations, 2019. Acceptance rate: 31% (500/1591) (Air Force project and ONR project)
  60. [ASPLOS’19] Ao Ren, Tianyun Zhang, Shaokai Ye, Jiayu Li, Wenyao Xu, Xuehai Qian, Xue Lin, and Yanzhi Wang. Admm-nn: An algorithm-hardware co-design framework of dnns using alternating direction methods of multipliers. In Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems, pages 925–938. ACM, 2019. Acceptance rate: 21.1% (74/350) (NSF AitF project)
  61. [HPCA’19] Zhe Li, Caiwen Ding, Siyue Wang, Wujie Wen, Youwei Zhuo, Chang Liu, Qinru Qiu, Wenyao Xu, Xue Lin, Xuehai Qian, and Yanzhi Wang. E-rnn: Design optimization for efficient recurrent neural networks in fpgas. In 2019 IEEE International Symposium on High Performance Computer Architecture, pages 69–80. IEEE, 2019. Acceptance rate: 19.7% (46/233) (NSF AitF project)
  62. [ASP-DAC’19Pu Zhao, Kaidi Xu, Sijia Liu, Yanzhi Wang, and Xue Lin. Admm attack: an enhanced adversarial attack for deep neural networks with undetectable distortions. In Proceedings of the 24th Asia and South Pacific Design Automation Conference, pages 499–505. ACM, 2019. (NSF AitF project, Air Force project, ONR project)
  63. [AAAI’19] Yanzhi Wang, Zheng Zhan, Liang Zhao, Jian Tang, Siyue Wang, Jiayu Li, Bo Yuan, Wujie Wen, and Xue Lin. Universal approximation property and equivalence of stochastic computing-based neural networks and binary neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 5369–5376, 2019. Acceptance rate: 16.2% (1150/7095) (NSF AitF)
  64. Siyue Wang, Xiao Wang, Shaokai Ye, Pu Zhao, and Xue Lin. Defending DNN adversarial attacks with pruning and logits augmentation. In Proc. of IEEE GlobalSIP 2018, Nov. 2018. (NSF AitF, Air Force, ONR)
  65. Pu Zhao, Kaidi Xu, Tianyun Zhang, Markan Fardad, Yanzhi Wang, and Xue Lin. Reinforced adversarial attacks on deep neural networks using ADMM. In Proc. of IEEE GlobalSIP 2018, Nov. 2018. (NSF AitF, Air Force, ONR)
  66. [ACM MM’18Pu Zhao, Sijia Liu, Yanzhi Wang, and Xue Lin. An admm-based universal framework for adversarial attacks on deep neural networks. In 2018 ACM Multimedia Conference on Multimedia Conference, pages 1065–1073. ACM, 2018. Acceptance rate: 27.5% (144/757) (NSF AitF, Air Force, ONR)
  67. [ICCAD’18][Best Paper NominationSiyue Wang, Xiao Wang, Pu Zhao, Wujie Wen, David Kaeli, Peter Chin, and Xue Lin. Defensive dropout for hardening deep neural networks under adversarial attacks. In Proceedings of the International Conference on Computer-Aided Design, page 71. ACM, 2018. Acceptance rate: 25% (98/396) (NSF AitF, Air Force, ONR)
  68. [AAAI’18] Yanzhi Wang, Caiwen Ding, Zhe Li, Geng Yuan, Siyu Liao, Xiaolong Ma, Bo Yuan, Xuehai Qian, Jian Tang, Qinru Qiu, and Xue Lin. Towards ultra-high performance and energy efficiency of deep learning systems: an algorithm-hardware co-optimization framework. In Thirty-Second AAAI Conference on Artificial Intelligence, 2018. (NSF AitF)
  69. [ASP-DAC’18Pu Zhao, Yanzhi Wang, Naehyuck Chang, Qi Zhu, and Xue Lin. A deep reinforcement learning framework for optimizing fuel economy of hybrid electric vehicles. In 2018 23rd Asia and South Pacific Design Automation Conference, pages 196–202. IEEE, 2018. (NSF AitF)
  70. [ICCAD’17] Siyu Liao, Zhe Li, Xue Lin, Qinru Qiu, Yanzhi Wang, and Bo Yuan. Energy-efficient, high-performance, highly-compressed deep neural network design using block-circulant matrices. In 2017 IEEE/ACM International Conference on Computer-Aided Design, pages 458–465. IEEE, 2017. (NSF AitF)
  71. [MICRO’17] Caiwen Ding, Siyu Liao, Yanzhi Wang, Zhe Li, Ning Liu, Youwei Zhuo, Chao Wang, Xuehai Qian, Yu Bai, Geng Yuan, Jian Tang, Qinru Qiu, Xue Lin, and Bo Yuan. C ir cnn: accelerating and compressing deep neural networks using block-circulant weight matrices. In Proceedings of the 50th Annual IEEE/ACM International Symposium on Microarchitecture, pages 395–408. ACM, 2017. (NSF AitF)
  72. [ASP-DAC’17] Caiwen Ding, Ji Li, Weiwei Zheng, Naehyuck Chang, Xue Lin, and Yanzhi Wang. Algorithm accelerations for luminescent solar concentrator-enhanced reconfigurable onboard photovoltaic system. In 2017 22nd Asia and South Pacific Design Automation Conference, pages 318–323. IEEE, 2017.
  73. [ICCD’16] Xue Lin, Yuankun Xue, Paul Bogdan, Yanzhi Wang, Siddharth Garg, and Massoud Pedram. Power-aware virtual machine mapping in the data-center-on-a-chip paradigm. In 2016 IEEE 34th International Conference on Computer Design, pages 241–248. IEEE, 2016.
  74. [ICCD’16] Caiwen Ding, Hongjia Li, Weiwei Zheng, Yanzhi Wang, Naehyuck Chang, and Xue Lin. Luminescent solar concentrator-based photovoltaic reconfiguration for hybrid and plug-in electric vehicles. In 2016 IEEE 34th International Conference on Computer Design, pages 281–288. IEEE, 2016.
  75. [CLOUD’16] Xue Lin, Massoud Pedram, Jian Tang, and Yanzhi Wang. A profit optimization framework of energy storage devices in data centers: Hierarchical structure and hybrid types. In 2016 IEEE 9th International Conference on Cloud Computing, pages 640–647. IEEE, 2016.
  76. [ICCAD’15] Xue Lin, Paul Bogdan, Naehyuck Chang, and Massoud Pedram. Machine learning-based energy management in a hybrid electric vehicle to minimize total operating cost. In 2015 IEEE/ACM International Conference on Computer-Aided Design, pages 627–634. IEEE, 2015.
  77. [DAC’15] Yanzhi Wang, Xue Lin, Massoud Pedram, and Naehyuck Chang. Joint automatic control of the powertrain and auxiliary systems to enhance the electromobility in hybrid electric vehicles. In 2015 52nd ACM/EDAC/IEEE Design Automation Conference, pages 1–6. IEEE, 2015.
  78. [DATE’15] Xue Lin, Yanzhi Wang, Massoud Pedram, Jaemin Kim, and Naehyuck Chang. Event-driven and sensorless photovoltaic system reconfiguration for electric vehicles. In Proceedings of the 2015 Design, Automation & Test in Europe Conference & Exhibition, pages 19–24. EDA Consortium, 2015.
  79. [ICCAD’14] Xue Lin, Yanzhi Wang, Paul Bogdan, Naehyuck Chang, and Massoud Pedram. Reinforcement learning based power management for hybrid electric vehicles. In Proceedings of the 2014 IEEE/ACM International Conference on Computer-Aided Design, pages 32–38. IEEE Press, 2014.
  80. [ICCD’14] Xue Lin, Yanzhi Wang, Naehyuck Chang, and Massoud Pedram. Power supply and consumption co-optimization of portable embedded systems with hybrid power supply. In 2014 IEEE 32nd International Conference on Computer Design, pages 477–482. IEEE, 2014.
  81. [ISVLSI’14][Best Paper Award] Alireza Shafaei, Yanzhi Wang, Xue Lin, and Massoud Pedram. Fincacti: Architectural analysis and modeling of caches with deeply-scaled finfet devices. In 2014 IEEE Computer Society Annual Symposium on VLSI, pages 290–295. IEEE, 2014.
  82. [CLOUD’14][Top Paper Award] Xue Lin, Yanzhi Wang, Qing Xie, and Massoud Pedram. Energy and performance-aware task scheduling in a mobile cloud computing environment. In 2014 IEEE 7th International Conference on Cloud Computing, pages 192–199. IEEE, 2014.
  83. [ICCAD’13] Xue Lin, Yanzhi Wang, and Massoud Pedram. Joint sizing and adaptive independent gate control for finfet circuits operating in multiple voltage regimes using the logical effort method. In 2013 IEEE/ACM International Conference on Computer-Aided Design, pages 444–449. IEEE, 2013.
  84. [ISLPED’13] Xue Lin, Yanzhi Wang, Siyu Yue, Naehyuck Chang, and Massoud Pedram. A framework of concurrent task scheduling and dynamic voltage and frequency scaling in real-time embedded systems with energy harvesting. In International Symposium on Low Power Electronics and Design, pages 70–75. IEEE, 2013.
  85. [ICCAD’12] Xue Lin, Yanzhi Wang, Di Zhu, Naehyuck Chang, and Massoud Pedram. Online fault detection and tolerance for photovoltaic energy harvesting systems. In Proceedings of the International Conference on Computer-Aided Design, pages 1–6. ACM, 2012.
  86. [DAC’12] Xue Lin, Yanzhi Wang, Siyu Yue, Donghwa Shin, Naehyuck Chang, and Massoud Pedram. Near-optimal, dynamic module reconfiguration in a photovoltaic system to combat partial shading effects. In DAC Design Automation Conference 2012, pages 516–521. IEEE, 2012.

Book Chapter

  1. Shijin Duan, Zhengang Li, Yukui Luo, Mengshu Sun, Wenhao Wang, Xue Lin, and Xiaolin Xu. Machine learning in hardware security. In Machine Learning for Hardware Security, Springer, 2020.

Patent

  1. Yanzhi Wang, and Xue Lin. Computer-implemented methods and systems for compressing deep neural network models using alternating direction method of multipliers (ADMM). U.S. Patent and Trademark Office, 2020. (NSF AitF project)

Journal Papers

  1. [Submitted to T Signal Processing][Impact Factor: 5.23] Pranay Sharma, Kaidi Xu, Sijia Liu, Pin-Yu Chen, Xue Lin, and Pramod Varshney. Zeroth-order hybrid gradient descent: towards a principled black-box optimization framework.
  2. [Submitted to T NNLS’20][Impact Factor: 11.68] Kaidi Xu, Sijia Liu, Gaoyuan Zhang, Mengshu Sun, Pu Zhao, QuanfuFan, Chuang Gan, Yanzhi Wang, and Xue Lin. Interpreting adversarial examples by activation promotion and suppression. (Air Force Project)
  3. [Submitted to TODAES’21] Yifan Gong*, Geng Yuan*, Zheng Zhan, Wei Niu, Zhengang Li, Pu Zhao, Yuxuan Cai, Sijia Liu, Bin Ren, Xue Lin, Xulong Tang, and Yanzhi Wang. Automatic mapping of the best-suited DNN pruning schemes for real-time mobile acceleration. ACM Transactions on Design Automation of Embedded Systems. (NSF SHF project)
  4. [Electrical Engineering’21Mengshu Sun, Pu Zhao, and Xue Lin. Power management in hybrid electric vehicles using deep recurrent reinforcement learning. Accepted in Electrical Engineering.
  5. [T PAMI’21][Impact Factor: 17.86] Wei Niu, Zhengan Li, Xiaolong Ma, Peiyan Dong, Gang Zhou, Xuehai Qian, Xue Lin, Yanzhi Wang, and Bin Ren. GRIM: a general, real-time deep learning inference framework for mobile devices based on fine-grained structured weight sparsity. IEEE Transactions on Pattern Analysis and Machine Intelligence, DOI: 10.1109/TPAMI.2021.3089687. (NSF SHF project)
  6. [T NNLS’20][Impact Factor: 11.68] Tianyun Zhang, Shaokai Ye, Xiaoyu Feng, Xiaolong Ma, Kaiqi Zhang, Zhengang Li, Jian Tang, Sijia Liu, Xue Lin, Yongpan Liu, Makan Farda, and Yanzhi Wang. StructADMM: achieving ultra-high efficiency in structured pruning for DNNs. IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2020.3045153.
  7. [T NNLS’20][Impact Factor: 11.68] Xiaolong Ma, Sheng Lin, Shaokai Ye, Zhezhi He, Linfeng Zhang, Geng Yuan, Sia Huat Tan, Zhengang Li, Deliang Fan, Xuehai Qian, Xue Lin, Kaisheng Ma, and Yanzhi Wang. Non-structured DNN weight pruning – Is it beneficial in any platform? IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2021.3063265. (NSF SHF project)
  8. [Parallel Computing’20][Impact Factor: 1.12] Shi Dong, Pu Zhao, Xue Lin, and David Kaeli. Exploring GPU acceleration of deep neural networks using block circulant matrices. https://authors.elsevier.com/sd/article/S0167-8191(20)30090-9 (NSF AitF project)
  9. [PloS one’18Mengshu Sun, Yuankun Xue, Paul Bogdan, Jian Tang, Yanzhi Wang, and Xue Lin. Hierarchical and hybrid energy storage devices in data centers: Architecture, control and provisioning. PloS one, 13(1):e0191450, 2018.
  10. [T VLSI’18] Jaemin Kim, Donkyu Baek, Caiwen Ding, Sheng Lin, Donghwa Shin, Xue Lin, Yanzhi Wang, Young Hoo Cho, Sang Hyun Park, and Naehyuck Chang. Dynamic reconfiguration of thermoelectric generators for vehicle radiators energy harvesting under location-dependent temperature variations. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 26(7):1241–1253, 2018.
  11. [Design&Test’18] Caiwen Ding, Hongjia Li, Weiwei Zheng, Yanzhi Wang, and Xue Lin. Reconfigurable photovoltaic systems for electric vehicles. IEEE Design & Test, 35(6):37–43, 2018.
  12. [IET CPS’17Pu ZhaoXue Lin, Yanzhi Wang, Shuang Chen, and Massoud Pedram. Hierarchical resource allocation and consolidation framework in a multi-core server cluster using a markov decision process model. IET Cyber-Physical Systems: Theory & Applications, 2(3):118–126, 2017.
  13. [T SC’17] Siyu Liao, Yi Xie, Xue Lin, Yanzhi Wang, Min Zhang, and Bo Yuan. Reduced-complexity deep neural networks design using multi-level compression. IEEE Transactions on Sustainable Computing, 2017.
  14. [T CAD’16] Xue Lin, Yanzhi Wang, Naehyuck Chang, and Massoud Pedram. Concurrent task scheduling and dynamic voltage and frequency scaling in a real-time embedded system with energy harvesting. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 35(11):1890–1902, 2016.
  15. [T SE’15] Yanzhi Wang, Xue Lin, and Massoud Pedram. A near-optimal model-based control algorithm for households equipped with residential photovoltaic power generation and energy storage systems. IEEE Transactions on Sustainable Energy, 7(1):77–86, 2015.
  16. [T CAS-II’15] Qing Xie, Xue Lin, Yanzhi Wang, Shuang Chen, Mohammad Javad Dousti, and Massoud Pedram. Performance comparisons between 7-nm finfet and conventional bulk cmos standard cell libraries. IEEE Transactions on Circuits and Systems II: Express Briefs, 62(8):761–765, 2015.
  17. [T SC’14] Xue Lin, Yanzhi Wang, Qing Xie, and Massoud Pedram. Task scheduling with dynamic voltage and frequency scaling for energy minimization in the mobile cloud computing environment. IEEE Transactions on Services Computing, 8(2):175–186, 2014.
  18. [T EC’14] Yanzhi Wang, Xue Lin, and Massoud Pedram. A stackelberg game-based optimization framework of the smart grid with distributed pv power generations and data centers. IEEE Transactions on Energy Conversion, 29(4):978–987, 2014.
  19. [T VLSI’14] Yanzhi Wang, Xue Lin, Younghyun Kim, Qing Xie, Massoud Pedram, and Naehyuck Chang. Single-source, single-destination charge migration in hybrid electrical energy storage systems. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 22(12):2752–2765, 2014.
  20. [T CAD’14] Yanzhi Wang, Xue Lin, Younghyun Kim, Naehyuck Chang, and Massoud Pedram. Architecture and control algorithms for combating partial shading in photovoltaic systems. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 33(6):917–930, 2014.
  21. [T Smart Grid’14] Yanzhi Wang, Xue Lin, and Massoud Pedram. Adaptive control for energy storage systems in households with photovoltaic modules. IEEE Transactions on Smart Grid, 5(2):992–1001, 2014.
  22. [Design&Test’13] Xue Lin, Yanzhi Wang, Massoud Pedram, Jaemin Kim, and Naehyuck Chang. Designing fault-tolerant photovoltaic systems. IEEE Design & Test, 31(3):76–84, 2013.
  23. [ACS Nano’12] Yuchi Che, Chuan Wang, Jia Liu, Bilu Liu, Xue Lin, Jason Parker, Cara Beasley, H-S Philip Wong, and Chongwu Zhou. Selective synthesis and device applications of semiconducting single-walled carbon nanotubes using isopropyl alcohol as feedstock. Acs Nano, 6(8):7454–7462, 2012.
  24. [Nano Research’10] Chuan Wang, Koungmin Ryu, Lewis Gomez De Arco, Alexander Badmaev, Jialu Zhang, Xue Lin, Yuchi Che, and Chongwu Zhou. Synthesis and device applications of high-density aligned carbon nanotubes using low-pressure chemical vapor deposition and stacked multiple transfer. Nano Research, 3(12):831–842, 2010.