Publications

Total citation above 6,900 according to Google Scholar

Arxiv Submission:
  1. (Key Contribution Summary Paper) Shaoshan Liu, Bin Ren, Xipeng Shen, Yanzhi Wang, “CoCoPIE: Making Mobile AI Sweet as PIE — Compression-Compilation Co-Design Goes a Long Way“.
  2. (Demonstration Paper) 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
  3. (NEW) Zheng Zhan, Yifan Gong, Zhengang Li, Pu Zhao, Xiaolong Ma, Wei Niu, Xiaolin Xu, Bin Ren, Yanzhi Wang, and Xue Lin, “A Privacy-Preserving DNN Pruning and Mobile Acceleration Framework“.
  4. Yanzhi Wang et al., “Non-Structured DNN Weight Pruning Considered Harmful“.
  5. Xiaolong Ma, Zhengang Li, Yifan Gong, Tianyun Zhang, Wei Niu, Zheng Zhan, Pu Zhao, Jian Tang, Xue Lin, Bin Ren, and Yanzhi Wang, “BLK-REW: A Unified Block-based DNN Pruning Framework using Reweighted Regularization Method“.
  6. Zhengang Li, Yifan Gong, Xiaolong Ma, Sijia Liu, Mengshu Sun, Zheng Zhan, Zhenglun Kong, Geng Yuan, and Yanzhi Wang, “SS-Auto: A Single-Shot, Automatic Structured Weight Pruning Framework of DNNs with Ultra-High Efficiency“.
  7. Fu-Ming Guo, Sijia Liu, Finlay Mungall, Xue Lin, and Yanzhi Wang, “Reweighted Proximal Pruning for Large-Scale Language Representation“.
  8. Shaokai Ye, Xiaoyu Feng, Tianyun Zhang, Xiaolong Ma, Sheng Lin, Zhengang Li, Kaidi Xu, Wujie Wen, Sijia Liu, Jian Tang, Makan Fardad, Xue Lin, Yongpan Liu, Yanzhi Wang, “Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMM.” CoRR abs/1903.09769(2019).
  9. Xiaolong Ma, Geng Yuan, Sheng Lin, Zhengang Li, Hao Sun, Yanzhi Wang, “ResNet Can Be Pruned 60x: Introducing Network Purification and Unused Path Removal (P-RM) after Weight Pruning.” CoRRabs/1905.00136 (2019).
Representative Publications:
  1. 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 Proc. of European Conference on Computer Vision (ECCV), 2020. (Spotlight Presentation, Top 5%)
  2. Xiaolong Ma, Wei Niu, Tianyun Zhang, Sijia Liu, Sheng Lin, Hongjia Li, Xiang Chen, Jian Tang, Kaisheng Ma, Bin Ren, and Yanzhi Wang, “An Image Enhancing Pattern-based Sparsity for Real-Time Inference on Mobile Devices“, in Proc. of European Conference on Computer Vision (ECCV), 2020.
  3. 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 Proc. of IJCAI, 2020.
  4. Runbin Shi, Peiyan Dong, Tong Geng, Yuhao Ding, Xiaolong Ma, Martin Herbordt, Ang Li, Hayden So, and Yanzhi Wang, “CSB-RNN: A faster-than-realtime RNN Acceleration Framework with Compressed Structured Blocks”, to appear in Proc. of ICS 2020.
  5. 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” to appear in ASPLOS 2020. (Acceptance Rate: 17.4%)
  6. Chaoqun Chu, Yanzhi Wang, Yilong ZHao, Xiaolong Ma, Shaokai ye, Yunyan Hong, Xiaoyao Liang, Yinhe Han, Yun Chen, Xiaosong Cui, and Li Jiang, “PIM-Prune: Fine-grained DCNN pruning for crossbar-based process-in-memory architecture”, to appear in DAC 2020. (Acceptance Rate: 22%)
  7. Zhanhong Tan, Jiebo Song, Xiaolong Ma, Sia-Huat Tan, Hongyang Chen, Yuanqing Miao, Yifu Wu, Shaokai Ye, Yanzhi Wang, Dehui Li, and Kaisheng Ma, “PCNN: Pattern-based fine-grained regular pruning towards optimizing CNN accelerators“, to appear in DAC 2020. (Acceptance Rate: 22%)
  8. Peiyan Dong, Siyue Wang, Wei Niu, Chengming Zhang, Sheng Lin, Zhengang Li, Yifan Gong, Bin Ren, Xue Lin, Yanzhi Wang, Dingwen Tao, “RTMobile: Beyond Real-time Mobile Acceleration of RNNs for Speech Recognition“, to appear in DAC 2020. (Acceptance Rate: 22%)
  9. Youwei Zhuo, Jingji Chen, Qinyi Luo, Yanzhi Wang, Hailong Yang, Depei Qian, and Xuehai Qian, “SympleGraph: Distributed graph processing with precise loop-carried dependency guarantee”, to appear in PLDI, 2020.
  10. Pu Zhao et al., “Bridging mode connectivity in loss landscapes and adversarial robustness” in Proc. of International Conference on Learning Representations (ICLR), 2020.
  11. Fuxun Yu, Chenchen Liu, Di Wang, Yanzhi Wang, Xiang Chen, “AntiDOte: Attention-based dynamic optimization for neural network runtime efficiency”, in Proc. of DATE 2020. (Best Paper Nomination)
  12. Yanzhi Wang, “Towards ultra-efficient DNN inference acceleration on edge devices for wellbeing applications”, in Mobisys 2020 workshop on HealthDL.
  13. Xiaolong Ma, Fu-Ming Guo, Wei Niu, Xue Lin, Jian Tang, Kaisheng Ma, Bin Ren, and Yanzhi Wang, “PCONV: the missing but desirable sparsity in DNN weight pruning for real-time execution on mobile device“, to appear in AAAI 2020. (Acceptance Rate: 20.9%)
  14. Ning Liu, Xiaolong Ma, Zhiyuan Xu, Yanzhi Wang, Jian Tang, and Jieping Ye, “AutoCompress: an automatic DNN structured pruning framework for ultra-high compression rates“, to appear in AAAI 2020. (Acceptance Rate: 20.9%)
  15. Ao Ren, Tao Zhang, Yuhao Wang, Sheng Lin, Peiyan Dong, Yen-kuang Chen, Yuan Xie, and Yanzhi Wang, “DARB: a density-adaptive regular-block pruning for deep neural networks“, to appear in AAAI 2020. (Acceptance Rate: 20.9%)
  16. Siyu Liao, Jie Chen, Yanzhi Wang, Qinru Qiu, and Bo Yuan, “Embedding compression with isotropic iterative quantization“, to appear in AAAI 2020. (Acceptance Rate: 20.9%)
  17. Shaoshan Liu, Bin Ren, Xipeng Shen, Yanzhi Wang, “CoCoPIE: Enabling Real-Time AI on Off-the-Shelf Mobile Devices via Compression-Compilation Co-Design“, conditionally accepted in Communications of ACM (CACM), 2020.
  18. Burak Kakillioglu, Ao Ren, Yanzhi Wang, and Senem Velipasalar, “3D capsule networks for object classification with weight pruning”, accepted in IEEE Access, 2020. (Impact Factor 4.1)
  19. Yanzhi Wang et al., “Non-Structured DNN Weight Pruning Considered Harmful“, conditionally accepted in IEEE Trans. on Neural networks and Learning Systems, 2020 (Impact Factor 12.18)
  20. Yidong Liu, Siting Liu, Yanzhi Wang, Fabrizio Lombardi, and Jie Han, “A Survey of Stochastic Computing Neural Networks for Machine Learning Applications”, in IEEE Trans. on Neural networks and Learning Systems, 2020 (Impact Factor 12.18)
  21. Tianyun Zhang, Shaokai Ye, Xiaoyu Feng, Xiaolong Ma, Kaiqi Zhang, Zhengang Li, Jian Tang, Sijia Liu, Xue Lin, Yongpan Liu, Makan Fardad, and Yanzhi Wang, “StructADMM: Achieving Ultra-High Efficiency in Structured Pruning for DNNs”, conditionally accepted in IEEE Trans. on Neural networks and Learning Systems, 2020 (Impact Factor 12.18)
  22. Xuehai Qian, Yanzhi Wang, and Avinash Karanth, “Introduction to the Special Issue on Machine Learning Architectures and Accelerators”, in IEEE Trans. on Computers, 2020.
  23. Zhiyuan Xu, Dejun Yang, Jian Tang, Yinan Tang, Tongtong Yuan, Yanzhi Wang, and Guoliang Xue, “An actor-critic-based transfer learning framework for experience-driven networking”, conditionally accepted in IEEE/ACM Trans. on Networking, 2020.
  24. Shaokai Ye, Kaidi Xu, Sijia Liu, Hao Cheng, Jan-Henrik Lambrechts, Huan Zhang, Aojun Zhou, Kaisheng Ma, Yanzhi Wang, Xue Lin, “Second Rethinking of Network Pruning in the Adversarial Setting.” to appear in ICCV 2019. (Acceptance Rate: 25%)
  25. 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 ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2019. (Acceptance Rate: 17.4%)
  26. Ruizhe Cai, Ao Ren, Olivia Chen, Ning Liu, Caiwen Ding, Xuehai Qian, Jie Han, Wenhui Luo, Nobuyuki Yoshikawa, and Yanzhi Wang, “A stochastic-computing based deep learning framework using adiabatic quantum-flux-parametron superconducting technology,” in Proc. of International Symposium on Computer Architecture (ISCA), 2019. (Acceptance Rate: 16.9%)
  27. Zhe Li, Caiwen Ding, Siyue Wang, Wujie Wen, Youwei Zhuo, Qinru Qiu, Wenyao Xu, Xue Lin, Xuehai Qian, and Yanzhi Wang, “E-RNN: Design optimization for efficient recurrent neural networks in FPGAs,” in Proc. of High-Performance Computing Architecture (HPCA), 2019. (Acceptance Rate: 18%)
  28. Tianyun Zhang, Sijia Liu, Yanzhi Wang, and Makan Fardad, “Generation of low distortion adversarial attacks via convex programming,” in Proc. of International Conference on Data Mining (ICDM), 2019 (Acceptance Rate: 18.5%), also Best Paper Nomination (Finally Top 3 Paper) at AdvML Workshop at KDD 2019.
  29. Jinshan Yue, Ruoyang Liu, Wenyu Sun, Zhe Yuan, Zhibo Wang, Yung-Ning Tu, Yi-Ju Chen, Ao Ren, Yanzhi Wang, et al., “A 65nm 0.39-to-140.3TOPS/W 1-to-12b unified neural network processor using block-circulant-enabled transpose-domain acceleration with 8.1X higher TOPS/mm2 and 6T HBST-TRAM-based 2D data-reuse architecture,” in Proc. of International Solid-State Circuits Conference (ISSCC), 2019.
  30. Youwei Zhuo, Chao Wang, Mingxing Zhang, Rui Wang, Dimin Niu, Yanzhi Wang, and Xuehai Qian, “GraphQ: Scalable PIM-based graph processing,” in IEEE/ACM International Symposium on Microarchitecture (MICRO), 2019. (Acceptance Rate: 18.6%)
  31. Zihao Liu, Tao Liu, Qi Liu, Nuo Xu, Xue Lin, Yanzhi Wang, and Wujie Wen, “Feature distillation: DNN-oriented JPEG compression against adversarial examples,” in Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. (Acceptance Rate: 25.2%)
  32. Hao Tang, Dan Xu, Nice Sebe, Yanzhi Wang, Jason J. Corso, and Yan Yan, “Multi-channel attention selection GAN with cascaded semantic guidance for cross-view image translation,” in Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. (Oral presentation, acceptance Rate: 6%)
  33. Zihao Liu, Xiaowei Xu, Tao Liu, Qi Liu, Yanzhi Wang, Yiyu Shi, Wujie Wen, Meiping Huang, Haiyun Yuan, and Jian Zhuang, “Machine vision guided 3D medical image compression for efficient transmission and accurate segmentation in the clouds,” in Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. (Acceptance Rate: 25.2%)
  34. Kaidi 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 Proc. of the International Conference on Learning Representations (ICLR), 2019. (Acceptance Rate: 31%)
  35. Siyu Liao, Zhe Li, Liang Zhao, Qinru Qiu, Yanzhi Wang, and Bo Yuan, “CircConv: A structured convolution with low complexity,” in the Thirty Third AAAI Conference on Artificial Intelligence (AAAI), 2019. (Acceptance Rate: 16.2%)
  36. Yanzhi Wang, Zheng Zhan, Jian Tang, Bo Yuan, Liang Zhao, Wei Wen, Siyue Wang, and Xue Lin, “Universal approximation property and equivalence of stochastic computing-based neural networks and binary neural networks,” in the Thirty Third AAAI Conference on Artificial Intelligence (AAAI), 2019. (Acceptance Rate: 16.2%)
  37. Siyue Wang, Xiao Wang, Pin-Yu Chen, Yanzhi Wang, Xue Lin, and Peter Chin, “Protecting neural networks with hierarchical random switching: towards better robustness-accuracy trade-off for stochastic defenses,” in Proc. of International Joint Conferences on Artificial Intelligence Organization (IJCAI), 2019. (Acceptance Rate: 17.8%)
  38. Fuxun Yu, Zhuwei Qin, Chenchen Liu, Liang Zhao, Yanzhi Wang, and Xiang Chen, “Interpreting and evaluating neural network robustness,” in Proc. of International Joint Conferences on Artificial Intelligence Organization (IJCAI), 2019. (Acceptance Rate: 17.8%)
  39. Tao Liu, Wujie Wen, Lei Jiang, Yanzhi Wang, Chengmo Yang, and Gang Quan, “A fault-tolerant neural network architecture,” in Proc. of Design Automation Conference (DAC), 2019. (Acceptance Rate: 22%)
  40. Pu Zhao, Siyue Wang, Cheng Gongye, Yanzhi Wang, Yunsi Fei, and Xue Lin, “Fault sneaking attack: a stealthy framework for misleading deep neural networks,” in Proc. of Design Automation Conference (DAC), 2019. (Acceptance Rate: 22%)
  41. Caiwen Ding, Shuo Wang, Ning Liu, Kaidi Xu, Yanzhi Wang, and Yun (Eric) Liang, “REQ-YOLO: A resource-aware, efficient quantization framework for object detection on FPGAs,” in Proc. of ACM International Symposium on Field Programmable Gate Arrays (FPGA), 2019. (Acceptance Rate: 25%)
  42. Wei Niu, Xiaolong Ma, Yanzhi Wang, and Bin Ren, “26ms inference time for ResNet-50: Towards real-time execution of all DNNs on smartphone,” workshop paper of International Conference on Machine Learning (ICML), 2019.
  43. Sheng Lin, Xiaolong Ma, Shaokai Ye, Geng Yuan, Kaisheng Ma, and Yanzhi Wang, “Toward extremely low bit and lossless accuracy in DNNs with progressive ADMM,” workshop paper of International Conference on Machine Learning (ICML), 2019.
  44. Zhiyuan Xu, Jian Tang, Chengxiang Yin, Yanzhi Wang, and Guoliang Xue, “Experience-driven congestion control: when multi-path TCP meets deep reinforcement learning,” IEEE Journal on Selected Areas in Communications (JSAC), 2019.
  45. Chengxiang Yin, Jian Tang, Zhiyuan Xu, and Yanzhi Wang, “Memory augmented deep recurrent neural network for video question answering,” in IEEE Trans. on Neural networks and Learning Systems, 2019 (Impact Factor 12.18).
  46. Yi Qiang, Ao Ren, Xianzhe Zhang, Preyaa Patel, Xun Han, Kyung-Jin Seo, Zhan Shi, Yanzhi Wang, and Hui Fang, “Deep reinforcement learning for dynamic treatment regimes on medical registry data”, 2D Materials, 2019 (Impact Factor 6.9)
  47. Ning Liu, Ying Liu, Brent Logan, and Yanzhi Wang, “Deep reinforcement learning for dynamic treatment regimes on medical registry data”, Nature Scientific Reports, 2019 (Impact Factor 5.23)
  48. Olivia Chen, Ruizhe Cai, Yanzhi Wang, Fei Ke, Taiki Yamae, Ro Saito, Naoki Takeuchi, and Nobuyuki Yoshikawa, “Adiabatic quantum-flux-parametron: Towards building extremely energy-efficient circuits and systems,Nature Scientific Reports, 2019 (Impact Factor 5.23)
  49. Zhengxiong Li, Aditya Singh Rathore, Chen Song, Sheng Wei, Yanzhi Wang, and Wenyao Xu, “PrinTracker: Fingerprinting 3D printers using commodity scanners,” ACM Conference on Computer and Communications Security (CCS), 2018. (Acceptance Rate: 16%)
  50. Teng Li, Zhiyuan Xu, Jian Tang, and Yanzhi Wang, “Model-free control for distributed stream data processing using deep reinforcement learning,” in International Conference on Very Large Data Bases (VLDB), 2018. (Acceptance Rate: 21%)
  51. Tianyun Zhang, Shaokai Ye, Kaiqi Zhang, Jian Tang, Wujie Wen, Makan Fardad, and Yanzhi Wang, “A systematic DNN weight pruning framework using alternating direction method of multipliers,European Conference on Computer Vision (ECCV), 2018. (Acceptance Rate: 28%)
  52. Ruizhe Cai, Ao Ren, Ning Liu, Xuehai Qian, Massoud Pedram, and Yanzhi Wang, “VIBNN: Hardware acceleration of Bayesian neural networks,” in ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2018. (Acceptance Rate: 17.4%)
  53. Massoud Pedram and Yanzhi Wang, “Design automation methodology and tools for superconducting electronics,” in Proc. of International Conference on Computer Aided Design (ICCAD), 2018. (Acceptance Rate: 24%)
  54. Pu Zhao, Sijia Liu, Yanzhi Wang, and Xue Lin, “An ADMM-based universal framework for adversarial attacks on deep neural networks”, in Proc. of ACM Multimedia (ACM MM), 2018. (Acceptance Rate: 24%)
  55. Sheng Lin, Ning Liu, Mahdi Nazemi, Hongjia Li, Caiwen Ding, Yanzhi Wang, Massoud Pedram, “FFT-based deep learning deployment in embedded systems“, in Proc. of Design Automation and Test in Europe (DATE), 2018. (Best Paper Nomination)
  56. Yanzhi Wang, Caiwen Ding, Zhe Li, Geng Yuan, et al., “Towards ultra-high performance and energy efficiency of deep learning systems: an algorithm-hardware co-optimization framework,” in the Thirty Second AAAI Conference on Artificial Intelligence (AAAI), 2018. (Acceptance Rate: 25%)
  57. Youwei Zhuo, Jinglei Cheng, Qinyi Luo, Jidong Zhai, Yanzhi Wang, Zhongzhi Luan, and Xuehai Qian, “CSE: Parallel finite state machines with convergence set enumeration,” in IEEE/ACM International Symposium on Microarchitecture (MICRO), 2018. (Acceptance Rate: 18.6%)
  58. Zhe Li, Shuo Wang, Caiwen Ding, Qinru Qiu, Yanzhi Wang, and Yun (Eric) Liang, “Efficient recurrent neural networks using structured matrices in FPGAs,” in International Conference on Learning Representation (ICLR) (short paper), 2018.
  59. Shuo Wang, Zhe Li, Caiwen Ding, Bo Yuan, Qinru Qiu, Yanzhi Wang, and Yun (Eric) Liang, “C-LSTM: Enabling efficient LSTM using structured compression techniques on FPGAs,” in Proc. of ACM International Symposium on Field Programmable Gate Arrays (FPGA), 2018. (Acceptance Rate: 25%)
  60. Zihao Liu, Jie Xu, Lei Jiang, Yanzhi Wang, Gang Quan, and Wujie Wen, “DeepN-JPEG: A deep neural network favorable JPEG-based image compression framework,” in Design Automation Conference (DAC), 2018. (Acceptance Rate: 25%)
  61. Zhiyuan Xu, Jingsong Meng, Weiyi Zhang, Dejun Yang, Jian Tang, and Yanzhi Wang, “Experience-driven networking: a deep reinforcement learning based approach,” in IEEE International Conference on Computer Communications (INFOCOM), 2018. (Acceptance Rate: 17%)
  62. Qi Liu, Tao Liu, Zihao Liu, Yanzhi Wang, Yier Jin, and Wujie Wen, “Security analysis and enhancement of model compressed deep learning systems under adversarial attacks,” to appear in Proc. of Asia and South Pacific Design Automation Conference (ASP-DAC), 2018. (Best paper nomination)(Acceptance Rate: 28%)
  63. Yidong Liu, Siting Liu, Yanzhi Wang, Fabrizio Lombardi, and Jie Han, “A stochastic computational multi-layer perceptron with backward propagation,” in IEEE Trans. on Computers, 2018.
  64. Liang Zhao, Siyu Liao, Yanzhi Wang, Jian Tang, and Bo Yuan, “Theoretical properties for neural networks with weight matrices of low displacement rank,” in Proc. of International Conference on Machine Learning (ICML), 2017. (Oral Presentation, Acceptance Rate: 22%)
  65. Yanzhi Wang, Caiwen Ding, Siyu Liao, Zhe Li, Yu Bai, et al., “A Universal, Cross-Platform Inference Framework of Large-Scale Deep Learning Systems with Very High Performance and Energy Efficiency”, in IEEE/ACM International Symposium on Microarchitecture (MICRO), 2017. (Acceptance Rate: 18.6%)
  66. Ao Ren, Ji Li, Zhe Li, Caiwen Ding, Xuehai Qian, Qinru Qiu, Bo Yuan, and Yanzhi Wang, “SC-DCNN: Highly-scalable deep convolutional neural network using stochastic computing,” in ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2017. (Acceptance Rate: 17.4%)
  67. Ning Liu, Zhe Li, Zhiyuan Xu, Jielong Xu, Sheng Lin, Qinru Qiu, Jian Tang, and Yanzhi Wang, “A hierarchical framework of cloud resource allocation and power management using deep reinforcement learning,IEEE International Conference on Distributed Computing (ICDCS), 2017. (Acceptance Rate: 16.9%)
  68. Jing Wang, Jian Tang, Zhiyuan Xu, and Yanzhi Wang, “Spatiotemporal modeling and prediction in cellular networks: a big data enabled deep learning approach,IEEE International Conference on Computer Communications (INFOCOM), 2017. (Acceptance Rate: 17%)
  69. Sijia Liu, Ao Ren, Yanzhi Wang, and Pramod K.Varshney, “Ultra-fast robust compressive sensing based on memristor crossbars,” in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2017. (Best paper award, Best Student Presentation Award)(Rank top 3 in more than 2,000 submissions)
  70. 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 Proc. of International Conference on Computer Aided Design (ICCAD), 2017. (Acceptance Rate: 24%)
  71. Ahmed Shrestha, Khadeer Ahmed, Yanzhi Wang, and Qinru Qiu, “A spike-based long short-term memory on a neurosynaptic processor,” in Proc. of International Conference on Computer Aided Design (ICCAD), 2017. (Acceptance Rate: 24%)
  72. Hongjia Li, Tianshu Wei, Ruizhe Cai, Qi Zhu, and Yanzhi Wang, “Deep reinforcement learning meets cyber-physical systems: applications and embedded implementations,” in Proc. of International Conference on Computer Aided Design (ICCAD), 2017. (Invited Paper, Acceptance Rate of Conference: 24%)
  73. Tianshu Wei, Yanzhi Wang, and Qi Zhu, “Deep reinforcement learning for HVAC control in smart buildings,” in ACM/IEEE Design Automation Conference (DAC), 2017. (Acceptance Rate: 22%)
  74. Chen Pan, Mimi Xie, Yongpan Liu, Yanzhi Wang, et al., “A lightweight progress maximization scheduler for non-volatile processor under unstable energy harvesting” in Proc. of ACM SIGPLAN/SIGBED Conference on Languages, Compilers, Tools, and Theory for Embedded Systems (LCTES), 2017. (Acceptance Rate: 23%)
  75. Yanzhi Wang and Massoud Pedram, “Model-free reinforcement learning and Bayesian classification in system-level power management,IEEE Trans. on Computers, 2016.
  76. Tiansong Cui, Shuang Chen, Yanzhi Wang, Qi Zhu, Shahin Nazarian, and Massoud Pedram. “Optimal Co-Scheduling of HVAC Control and Battery Management for Energy-Efficient Buildings Considering State-of-Health Degradation,” in Proc. of Asia and South Pacific Design Automation Conf., Jan. 2016. (Best Paper Nomination) (Acceptance Rate: 33%)
  77. Xue Lin, Yanzhi Wang, Paul Bogdan, Naehyuck Chang, and Massoud Pedram, “Reinforcement learning based power management for hybrid electric vehicles,” in Proc. of International Conference on Computer Aided Design (ICCAD), 2014. (Acceptance Rate: 24%)
  78. Alireza Shafaei, Yanzhi Wang, Xue Lin, and Massoud Pedram, “FinCACTI: Architectural analysis and modeling of caches with deeply-scaled FinFET devices,” in Proc. of IEEE Computer Society Annual Symposium on VLSI (ISVLSI), 2014. (Best paper award)(Full Paper Acceptance Rate: 28%)
  79. Jaemin Kim, Yanzhi Wang, Massoud Pedram, and Naehyuck Chang, “Fast photovoltaic array reconfiguration for partial solar powered vehicles,” in Proc. of IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED), 2014. (Best paper award)(Full Paper Acceptance Rate: 24%)
  80. Xue Lin, Yanzhi Wang, Qing Xie, and Massoud Pedram, “Energy and performance-aware task scheduling framework in the mobile cloud computing environment,” in Proc. of IEEE Cloud Computing Conference (IEEE Cloud), 2014. (Top Paper Award)(Research Track Acceptance Rate: 18%)
  81. 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, 2014. (TCAD popular paper)
  82. Woojoo Lee, Yanzhi Wang, Donghwa Shin, Naehyuck Chang, and Massoud Pedram, “Optimizing the power delivery network in a smartphone platform,IEEE Transactions on Computer Aided Design of Integrated Circuits and Systems, 2014. (TCAD popular paper)
  83. Qing Xie, Yanzhi Wang, and Massoud Pedram, “Variability-aware design of energy-delay optimal linear pipelines operating in the near-threshold regime and above,” in Proc. of ACM Great Lakes Symposium on VLSI (GLSVLSI), 2013. (Best paper nomination)(Oral Presentation Paper Acceptance Rate: 24%)
  84. Sangyoung Park, Jaehyun Park, Donghwa Shin, Yanzhi Wang, Qing Xie, Naehyuck Chang, and Massoud Pedram, “Accurate modeling of the delay and energy overhead of dynamic voltage and frequency scaling in modern microprocessors,” in IEEE Transactions on Computer Aided Design of Integrated Circuits and Systems, 2013. (Best paper nomination)
  85. Yanzhi Wang, Qing Xie, Ahmed Ammari, and Massoud Pedram, “Deriving a near-optimal power management policy using model-free reinforcement learning and Bayesian classification,Proc. of Design Automation Conference (DAC), Jun. 2011. (Acceptance Rate: 22%)
Selected Prior Journal and Conference Publications:
  1. 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 Trans. on Sustainable Energy, 2016. (Impact Factor: 3.73)
  2. Yanzhi Wang, Xue Lin, and Massoud Pedram, “A model-based control algorithm for households equipped with residential photovoltaic power generation and energy storage systems,” in IEEE Trans. on Sustainable Energy, 2015. (Impact Factor: 3.73)
  3. Xue Lin, Yanzhi Wang, Qing Xie, and Massoud Pedram, “An energy and performance-aware task scheduling framework in the mobile cloud computing environment,” in IEEE Transactions on Service Computing, 2015. (Invited Paper)
  4. Yanzhi Wang, Xue Lin, and Massoud Pedram, “Adaptive control for energy storage systems in households with photovoltaic modules”, IEEE Transactions on Smart Grid, 2014. (Impact Factor: 3.19)
  5. Younghyun Kim, Yanzhi Wang, Massoud Pedram, and Naehyuck Chang, “Computer-aided design and optimization of hybrid energy storage systems,Foundations and Trends in Electronic Design Automation, 2013.
  6. Yanzhi Wang, Xue Lin, Naehyuck Chang, and Massoud Pedram, “Joint automatic control of power train and auxiliary systems in an intelligent HEV for enhancing electromobility,” in Proc. of Design Automation Conference (DAC), 2015. (Acceptance Rate: 22%)
  7. Shuang Chen, Yanzhi Wang, and Massoud Pedram, “Optimal offloading control for a mobile device based on a realistic battery model and semi-Markov decision process,” in Proc. of International Conference on Computer Aided Design (ICCAD), Nov. 2014. (Acceptance Rate: 24%)
  8. Xue Lin, Yanzhi Wang, and Massoud Pedram, “Joint sizing and adaptive independent gate control for FinFET circuits operating in multiple voltage regimes using logical effort method,” in Proc. of International Conference on Computer-Aided Design (ICCAD), Nov. 2013. (Acceptance Rate: 24%)
  9. Qing Xie, Jaemin Kim, Yanzhi Wang, Donghwa Shin, Naehyuck Chang, and Massoud Pedram, “Dynamic thermal management in mobile devices considering the thermal coupling between battery and application processor,” in Proc. of International Conference on Computer-Aided Design (ICCAD), Nov. 2013. (Acceptance Rate: 24%)
  10. Xue Lin, Yanzhi Wang, Di Zhu, Naehyuck Chang, and Massoud Pedram, “Online fault detection and tolerance in photovoltaic energy harvesting systems,” in Proc. of International Conference on Computer-Aided Design (ICCAD), Nov. 2012. (Acceptance Rate: 24%)
  11. Yanzhi Wang, Xue Lin, Naehyuck Chang, and Massoud Pedram, “Dynamic reconfiguration of photovoltaic energy harvesting system in hybrid electric vehicles,” in Proc. of the International Symposium on Low Power Electronics and Design (ISLPED), 2012. (Full Paper Acceptance Rate: 24%)
  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 Proc. of Design Automation Conference (DAC), June 2012. (Acceptance Rate: 22%)