Publications

Journal Articles

• [IEEE-TCAD ‘24] ^* F. Yu, Z. Xu, L. Shangguan, D. Wang, D. Stamoulis, R. Madhok, N. Karianakis, A. Li, C. Liu, Y. Chen, and X. Chen. “Rethinking Latency-Aware DNN Design With GPU Tail Effect Analysis,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, May 2024.
• [IEEE-TCCN ‘24] ^* W. Zhang, Y. Wang, X. Chen, L. Liu, and Z. Tian. “Collaborative Learning based Spectrum Sensing under Partial Observations,” IEEE Transactions on Cognitive Communications and Networking, Apr. 2024.
• [IEEE-TNNLS ‘23] ^* P. Xu, Y. Wang, X. Chen, and Z. Tian. “QC-ODKLA: Quantized and Communication-Censored Online Decentralized Kernel Learning via Linearized ADMM,” IEEE Transactions on Neural Networks and Learning Systems, Sep. 2023.
• [IEEE-TC ‘22] ^† Z. Xu, F. Yu, J. Xiong, and X. Chen. “HeliosX: Heterogeneity-Aware Federated Learning for Dynamic Edge Collaboration Management,” IEEE Transactions on Computers, to appear.
• [IEEE-TC ‘22] ^† F. Yu, W. Zhang, Z. Qin, Z. Xu, D. Wang, C. Liu, Z. Tian, and X. Chen. “Fed$^2$X: Feature-Aligned Large-Scale Federated Learning Systems,” IEEE Transactions on Computers, to appear.
• [HPC ‘22] ^† F. Yu, Z. Xu, Z. Qin, and X. Chen. “Privacy-Preserving Federated Learning for Transportation Mode Prediction based on Personal Mobility Data,” High-Confidence Computing, Dec. 2022.
• [ACM-TECS ‘22] ^† Z. Xu, F. Yu, and X. Chen. “LanCeX: A Versatile and Lightweight Defense Method against Condensed Adversarial Attacks,” ACM Transactions on Embedded Computing Systems, Aug. 2022.
• [IEEE-TCAD ‘22] ^† F. Yu, C. Liu, D. Wang, Y. Wang and X. Chen. “AntiDoteX: Attention-based Neural Network Runtime Efficiency Dynamic Optimization,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Jan. 2022.
• [JMLR ‘21] ^* P. Xu, Y. Wang, X. Chen, and Z. Tian. “COKE: Communication-Censored Decentralized Kernel Learning,” Journal of Machine Learning Research, Vol. 22 No. 196, pp. 1~35, Oct. 2021.
• [IEEE-TCAD ‘21] ^† Z. Qin, F. Yu, Z. Xu, C. Liu, and X. Chen. “CaptorX: A Class-Adaptive Convolutional Neural Network Reconfiguration Framework,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Feb. 2021.
• [IEEE-TCAD ‘20] ^† F. Yu, Z. Qin, C. Liu, D. Wang, and X. Chen. “REIN the RobuTS: Robust DNN-based Image Recognition in Autonomous Driving Systems,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Oct. 2020.
• [IEEE-TCAD ‘20] ^† Z. Xu, F. Yu, C. Liu, and X. Chen. “DiReCtX: Dynamic Resource-Aware CNN Reconfiguration Framework for Real-Time Mobile Application,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, May 2020.
• [GMU-JSSR ‘19] ^† R. Yu, L. Punya, K. Wang, Z. Qin, and X. Chen. “CAPTURE: An End-to-End Mobile Implementation for a Computationally Optimized Deep Learning Framework,” Journal of Student-Scientists’ Research, George Mason University, Nov. 2019.
• [AMC-MFC ‘18] ^† Z. Qin, F. Yu, C. Liu, and X. Chen. “How Convolutional Neural Networks See the World — A Survey of Convolutional Neural Network Visualization Methods,” Advances in Mathematics of Communications Journal on Mathematical Foundations of Computing, Vol. 1, Iss. 2, No. 149, pp. 149~180, May 2018.
• [ACM-ETCS ‘12] Z. Sun, X. Chen, Y. Zhang, H. Li, and Y. Chen. “Non-volatile Memories as the Data Storage System for Implantable ECG Recorder,” ACM Journal on Emerging Technologies in Computing Systems, Vol. 8, Iss. 2, No. 13, pp. 1~16, Jun. 2012.

Conference Papers

• [ICML ‘24] ^† C. Xu, F. Yu, Z. Xu, N. Inkawhich, and X. Chen. “Out-of-Distribution Detection via Deep Multi-Comprehension Ensemble,” in Proceedings of the International Conference on Machine Learning, to appear, 2024.
• [ICASSP ‘24] ^* P. Xu, Y. Wang, X. Chen, and Z. Tian. “Communication-Efficient Decentralized Dynamic Kernel Learning,” in Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, pp. 7135~7139, 2024.
• [ASP-DAC ‘24] ^† C. Xu, F. Yu, Z. Xu, C. Liu, J. Xiong, and X. Chen. “QuadraNet: Improving High-Order Neural Interaction Efficiency with Hardware-Aware Quadratic Neural Networks,” in Proceedings of the Asia and South Pacific Design Automation Conference, pp. 19~25, 2024.
• [ICML ‘23] J. Zhang, A. Li, M. Tang, J. Sun, X. Chen, F. Zhang, C. Chen, Y. Chen, and H. Li. “Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction,” in Proceedings of the International Conference on Machine Learning, pp. 41354~41381, 2023.
• [DAC ‘23] ^† Y. Yu, F. Yu, C. Liu, X. Sheng, and X. Chen. “EagleRec: Edge-Scale Recommendation System Acceleration with Inter-Stage Parallelism Optimization on GPUs,” in Proceedings of the Design Automation Conference, pp. 1~6, 2023.
• [ICCASP ‘22] ^* P. Xu, Y. Wang, X. Chen, and Z. Tian. “Deep Kernel Learning Network with Multiple Learning Paths,” in Proceedings of the International Conference on Acoustics, Speech, \& Signal Processing, pp. 4438~4442, 2022.
• [ICC ‘22] ^* W. Zhang, Y. Wang, F. Yu, Z. Qin, X. Chen, and Z. Tian. “Wideband Spectrum Sensing based on Collaborative Multi-Task Learning,” in Proceedings of the International Conference on Communication, pp. 1~6, 2022.
• [MLSys ‘22] ^† Z. Xu, F. Yu, J. Xiong, and X. Chen. “QuadraLib: A Performant Quadratic Neural Network Library for Architecture Optimization and Design Exploration,” in Proceedings of the Conference on Machine Learning and Systems, pp. 503~514, 2022.
• [WACV ‘22] ^† Z. Xu, F. Yu, C. Liu, H. Wang, and X. Chen. “FalCon: Fine-grained Feature Map Sparsity Computing with Decomposed Convolutions for Inference Optimization,” in Proceedings of the Winter Conference on Applications of Computer Vision, pp. 3634~3644, 2022.
• [WACV ‘22] ^† F. Yu, D. Wang, Y. Chen, N. Karianakis, T. Shen, P Yu, D. Lymberopoulos, S. Lu, W. Shi, and X. Chen. “SC-UDA: Style and Content Gap Aware Unsupervised Domain Adaptation for Object Detection,” in Proceedings of the Winter Conference on Applications of Computer Vision, pp. 1061~1070, 2022.
• [ICCAD ‘21] ^† F. Yu, S. Bray, D. Wang, L. Shangguan, X. Tang, C. Liu, and X. Chen. “Automated Runtime-Aware Scheduling for Multi-Tenant DNN Inference on GPU,” in Proceedings of the International Conference on Computer-Aided Design, pp. 1~9, 2021.
• [DAC ‘21] ^† Z. Xu, F. Yu, J. Xiong, and X. Chen. “Helios: Heterogeneity-Aware Federated Learning with Dynamically Balanced Collaboration,” in Proceedings of the Design Automation Conference, pp. 997~1022, 2021.
• [KDD ‘21] ^† F. Yu, W. Zhang, Z. Qin, Z. Xu, D. Wang, C. Liu, Z. Tian, and X. Chen. “Fed$^2$: Feature-Aligned Federated Learning,” in Proceedings of the ACM SigKDD Conference on Knowledge Discovery and Data Mining, pp. 2066~2074, 2021.
• [SEC ‘20] ^† F. Yu, D. Stamoulis, D. Wang, D. Lymberopoulos, and X. Chen. “Exploring the Design Space of Efficient Deep Neural Networks,” in Proceedings of the ACM/IEEE Symposium on Edge Computing, pp. 317~318, 2020.
• [SEC ‘20] ^◦ X. Chen and Z. Qin. “Exploring Decentralized Collaboration in Heterogeneous Edge Training,” in Proceedings of the ACM/IEEE Symposium on Edge Computing, pp. 450~453, 2020.
• [ISLPED ‘20] C. Liu, F. Yu, Z. Qin, and X. Chen. “Enabling Efficient ReRAM-based Neural Network Computing via Crossbar Structure Adaptive Optimization,” in Proceedings of the ACM/IEEE International Symposium on Low Power Electronics and Design, pp. 133~138, 2020.
• [ECCV ‘20] X. Ma, W. Niu, T. Zhang, S. Liu, S. Lin, H. Li, X. Chen, J. Tang, K. Ma, B. Ren, and Y. Wang. “An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices,” in Proceedings of the European Conference on Computer Vision, pp. 629~645, 2020.
• [DATE ‘20] ^† F. Yu, C. Liu, D. Wang, Y. Wang, and X. Chen. “AntiDOte: Attention-based Dynamic Optimization for Neural Network Runtime Efficiency,” in Proceedings of the Design Automation and Test in Europe Conference, pp. 951~956, 2020.
• [DATE ‘20] ^† F. Yu, Z. Qin, D. Wang, P. Xu, C. Liu, Z. Tian, and X. Chen. “DC-CNN: Computational Flow Redefinition for Efficient CNN Inference through Model Structural Decoupling,” in Proceedings of the Design Automation and Test in Europe Conference, pp. 1097~1102, 2020.
• [ASP-DAC ‘20] ^† Z. Xu, F. Yu, C. Liu, and X. Chen. “LanCe: A Comprehensive and Lightweight CNN Defense Methodology against Physical Adversarial Attacks on Embedded Multimedia Applications,” in Proceedings of the Asia and South Pacific Design Automation Conference, pp. 470~475, 2020.
• [ASP-DAC ‘20] X. Ma, G. Yuan, S. Lin, C. Ding, F. Yu, T. Liu, W. Wen, X. Chen, and Y. Wang. “Tiny but Accurate: A Pruned, Quantized and Optimized Memristor Crossbar Framework for Ultra Efficient DNN Implementation,” in Proceedings of the Asia and South Pacific Design Automation Conference, pp. 301~306, 2020.
• [CIKM ‘19] X. Guo, A. Alipour-Fanid, L. Wu, H. Purohit, X. Chen, K. Zeng, and Liang Zhao. “Multi-stage Deep Classifier Cascades for Open World Recognition,” in Proceedings of the ACM International Conference on Information and Knowledge Management, pp. 179~188, 2019.
• [BMVC ‘19] ^† Z. Qin, F. Yu, C. Liu, and X. Chen. “Functionality-Oriented Convolutional Filter Pruning,” in Proceedings of the British Machine Vision Conf., No. 229, 2019.
• [IJCAI ‘19] ^† F. Yu, Z. Qin, C. Liu, L. Zhao, Y. Wang, and X. Chen. “Interpreting and Evaluating Neural Network Robustness,” in Proceedings of the International Joint Conference on Artificial Intelligence, pp. 4199~4205, 2019.
• [KDD ‘19] J. Wang, F. Yu, X. Chen, and L. Zhao. “ADMM for Efficient Deep Learning with Global Convergence,” in Proceedings of the ACM SigKDD Conference on Knowledge Discovery and Data Mining, pp. 111~119, 2019.
• [DAC ‘19] ^† Z. Xu, F. Yu, C. Liu, and X. Chen. “ReForm: Static and Dynamic Resource-Aware DNN Reconfiguration Framework for Mobile Devices,” in Proceedings of the Design Automation Conference, pp. 183:1~183:6, 2019.
• [DAC ‘19] ^† Z. Xu, F. Yu, C. Liu, and X. Chen. “MASKER: Adaptive Mobile Security Enhancement against Automatic Speech Recognition in Eavesdropping,” in Proceedings of the Design Automation Conference, pp. 163:1~163:6, 2019.
• [ASP-DAC ‘19] ^† Z. Xu, F. Yu, C. Liu, and X. Chen. “HAMPER: High-Performance Adaptive Mobile Security Enhancement against Malicious Speech and Image Recognition,” in Proceedings of the Asia and South Pacific Design Automation Conf., pp. 512~517, 2019.
• [ASP-DAC ‘19] ^† F. Yu, C. Liu, and X. Chen. “REIN: A Robust Training Method for Enhancing Generalization Ability of Neural Networks in Autonomous Driving Systems,” in Proceedings of the Asia and South Pacific Design Automation Conf., pp. 456~461, 2019.
• [ASP-DAC ‘19] ^† Z. Qin, F. Yu, C. Liu, and X. Chen. “CAPTOR: A Class Adaptive Filter Pruning Framework for Convolutional Neural Networks in Mobile Applications,” in Proceedings of the Asia and South Pacific Design Automation Conf., pp. 444~449, 2019.
• [GlobalSIP ‘18] ^* Z. Zhang, X. Chen, and Z. Tian. “A Hybrid Neural Network Framework and Application to Radar Automatic Target Recognition,” in Proceedings of the IEEE Global Conference on Signal and Information Processing, pp. 246~250, 2018.
• [ISLPED ‘18] ^† Z. Xu, Z. Qin, F. Yu, C. Liu, and X. Chen. “DiReCt: Resource-Aware Dynamic Model Reconfiguration for Convolutional Neural Network in Mobile Systems,” in Proceedings of the ACM/IEEE International Symposium on Low Power Electronics and Design, pp. 37:1~37:6, 2018.
• [ISVLSI ‘18] C. Liu, Q. Dong, F. Liu, F. Yu, and X. Chen. “ReRise: An Adversarial Example Restoration System for Neuromorphic Computing Security,” in Proceedings of the IEEE Computer Society Annual Symposium on VLSI, pp. 470~475, 2018.
• [ICCAD ‘17] ^* J. Mao, Z. Qin, Z. Xu, K. Nixon, X. Chen, H. Li, and Y. Chen. “AdaLearner: An Adaptive Distributed Mobile Learning System for Neural Networks,” in Proceedings of the International Conf.\ on Computer-Aided Design, pp. 291~296, 2017.
• [ICCAD ‘17] ^† Z. Qin, Z. Xu, Q. Dong, Y. Chen, and X. Chen. “VoCaM: Visualization Oriented Convolutional Neural Network Acceleration on Mobile Systems,” in Proceedings of the International Conference on Computer-Aided Design, pp. 835~840, 2017.
• [ICCAD ‘17] ^* J. Mao, Z. Yang, W. Wen, C. Wu, L. Song, K. Nixon, X. Chen, H. Li, and Y. Chen. “MeDNN: A Distributed Mobile System with Enhanced Partition and Deployment for Large-Scale DNNs,” in Proceedings of the International Conference on Computer-Aided Design, pp. 751~756, 2017.
• [SoCC ‘17] L. Broyde, K. Nixon, X. Chen, and H. Li. “MobiCore: An Adaptive Hybrid Approach for Power-Efficient CPU Management on Android Devices,” in Proceedings of the IEEE International System-on-Chip Conference, pp. 221~226, 2017.
• [DATE ‘17] ^* J. Mao, X. Chen, K. Nixon, C. Krieger, and Y. Chen. “MoDNN: Local Distributed Mobile Computing System for Deep Neural Network,” in Proceedings of the Design Automation and Test in Europe Conference, pp. 1396~1401, 2017.
• [ICCAD ‘16] K. Nixon, X. Chen, and Y. Chen. “Scope: Quality Retaining Display Rendering Workload Scaling based on User-Smartphone Distance,” in Proceedings of the International Conference on Computer-Aided Design, pp. 1~6, 2016.
• [RSP ‘16] ^◦ X. Chen, J. Mao, K. Nixon, and Y. Chen. “MORPh: Mobile OLED Power Friendly Camera System,” in Proceedings of the International Symposium on Rapid System Prototyping, pp. 7~11, 2016.
• [SoCC ‘16] ^◦ X. Chen, K. Nixon, and Y. Chen. “Practical Power Consumption Analysis with Current Smartphones,” in Proceedings of the IEEE International System-on-Chip Conference, pp. 333~337, 2016.
• [DAC ‘16] ^◦ X. Chen, J. Mao, J. Gao, K. Nixon, and Y. Chen. “MORPh: Mobile OLED-friendly Recording and Playback System for Low Power Video Streaming,” in Proceedings of the Design Automation Conference, pp. 1~6, 2016.
• [ASP-DAC ‘16] K. Nixon, X. Chen, and Y. Chen. “Footfall: GPS Polling Scheduler for Power Saving on Wearable Devices,” in Proceedings of the Asia and South Pacific Design Automation Conference, pp. 563~568, 2016.
• [ASP-DAC ‘16] K. Nixon, X. Chen, and Y. Chen. “SlowMo: Enhancing Mobile Gesture-based Authentication Schemes via Sampling Rate Optimization,” in Proceedings of the Asia and South Pacific Design Automation Conference, pp. 462~467, 2016.
• [DAC ‘15] ^◦ X. Chen, J. Xue, and Y. Chen. “DaTuM: Dynamic Tone Mapping Technique for OLED Display Power Saving based on Video Classification,” in Proceedings of Design Automation Conference, pp. 8~12, 2015.
• [DAC ‘14] ^◦ X. Chen, M. Dong, C. Zhang, and Y. Chen. “Demystify Smartphone Power Consumption: The Evolution of Smartphone Communication Modules,” in Proceedings of the Design Automation Conference, 2014.
• [CODES+ISSS ‘13] M. Zhao, X. Chen, Y. Chen, and J. Xue. “Online OLED Dynamic Voltage Scaling for Video Streaming Applications on Mobile Devices,” in Proceedings of International Conf. on Hardware/Software Co-design and System Synthesis, pp. 1~10, 2013.
• [RTSS ‘13] M. Zhao, X. Chen, Y. Chen, and J. Xue. “Online OLED Dynamic Voltage Scaling for Video Streaming Applications on Mobile Devices,” in Proceedings of the IEEE Real-Time Systems Symposium, Vol. 10, Iss. 2, No. 18, 2013.
• [ASP-DAC ‘13] K. Nixon, X. Chen, Z.~H. Mao, Y. Chen, and K. Li. “Mobile User Classification and Authorization based on Gesture Usage Recognition,” in Proceedings of the Asia and South Pacific Design Automation Conference, pp. 384~389, 2013.
• [ICCAD ‘12] ^◦ X. Chen, J. Xue, and Y. Chen. “Mobile Devices User — The Subscriber and also the Publisher of Real-Time OLED Display Power Management Plan,” in Proceedings of the International Conference on Computer-Aided Design, pp. 687~690, 2012.
• [ICCAD ‘12] ^◦ X. Chen, B. Liu, M. Zhao, J. Xue, X. Guo, and Y. Chen. “Active Compensation Technique for the Thin-Film Transistor Variations and OLED Aging of Mobile Device Displays,” in Proceedings of the International Conference on Computer-Aided Design, pp. 516~522, 2012.
• [DAC ‘12] ^◦ X. Chen, M. Zhao, J. Zeng, J. Xue, and Y. Chen. “Quality-Retaining OLED Dynamic Voltage Scaling for Video Streaming Applications on Mobile Devices,” in Proceedings of the Design Automation Conference, pp. 1000~1005, 2012.
• [ASP-DAC ‘12] ^◦ X. Chen, J. Zeng, Y. Chen, and H. Li. “Fine-grained Dynamic Voltage Scaling on OLED Display,” in Proceedings of the Asia and South Pacific Design Automation Conference, pp. 807~812, 2012.
• [CICC ‘11] P. Wang, X. Chen, Y. Chen, H. Li, S. Kang, X. Zhu, and W. Wu. “A 1.0V 45nm Nonvolatile Magnetic Latch Design and Its Robustness Analysis,” in Proceedings of the IEEE Custom Integrated Circuits Conference, pp. 1~4, 2011.
• [WCSE ‘09] ^◦ X. Chen, Z. Zhang, and R. Chen. “A Real-Time Driving Fatigue Monitoring DSP Device based on Computing Complexity of Binarized Image,” in Proceedings of IEEE International Workshop on Computer Science and Engineering, pp. 84~89, 2009.

Workshop Publications

• [SPAWC ‘23] ^* W. Zhang, Y. Wang, X. Chen, and Z. Tian. “Spectrum Transformer: Wideband Spectrum Sensing using Multi-Head Self-Attention,” in Proceedings of the IEEE International Workshop on Signal Processing Advances in Wireless Communications, Aug. 2022.
• [SPAWC ‘23] ^* W. Zhang, Y. Wang, X. Chen, and Z. Tian. “Spectrum Transformer: Wideband Spectrum Sensing using Multi-Head Self-Attention,” in Proceedings of the IEEE International Workshop on Signal Processing Advances in Wireless Communications, Sep. 2023.
• [MLSys-CI ‘22] ^† F. Yu, D. Wang, L. Shangguan, M. Zhang, C. Liu, T. Soyata, and X. Chen. “A Survey of Multi-Tenant Deep Learning Inference on GPU,” in Proceedings of the Conference on Machine Learning and Systems, Workshop on Cloud Intelligence / AIOps, Aug. 2022.
• [CVPR-V4AS ‘20] ^† F. Yu, D. Wang, Y. Chen, N. Karianakis, P. Yu, D. Lymberopoulos, and X. Chen. “Unsupervised Domain Adaptation for Object Detection via Cross-Domain Semi-Supervised Learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Workshop on Vision for all Seasons: Adverse Weather and Lighting Conditions, Jun. 2020.
• [SEC ‘19] ^† Z. Qin, F. Yu, and X. Chen. “Task-Adaptive Incremental Learning for Intelligent Edge Devices,” in Proceedings of the ACM/IEEE Symposium on Edge Computing, pp. 340~341, Nov. 2019.
• [KDD-AIoT ‘19] ^† Z. Xu, F. Yu, and X. Chen. “DoPa: A Fast and Comprehensive CNN Defense Methodology against Physical Adversarial Attacks,” in Proceedings of the ACM SigKDD Conference on Knowledge Discovery and Data Mining, Workshop on Artificial Intelligence of Things, No. 3, Aug. 2019.
• [NIPS-CDNNIA ‘18] ^† Z. Qin, F. Yu, C. Liu, and X. Chen. “Demystifying Neural Network Filter Pruning,” in Proceedings of the Conference on Neural Information Processing Systems, Workshop on Compact Deep Neural Networks with Industrial Applications, No.24, Dec. 2018.
• [NIPS-CDNNIA ‘18] ^† F. Yu, Z. Qin, and X. Chen. “Distilling Critical Paths in Convolutional Neural Networks,” in Proceedings of the Conference on Neural Information Processing Systems, Workshop on Compact Deep Neural Networks with Industrial Applications, No. 34, Dec. 2018.
• [USENIX HotPower ‘14] ^◦ X. Chen, K. Nixon, H. Zhou, Y. Liu, and Y. Chen. “FingerShadow: An OLED Power Optimization based on Smartphone Touch Interactions,” in Proceedings of the International Workshop on Power-Aware Computing and System, No. 6, Oct. 2014.
• [USENIX HotPower ‘14] K. Nixon, X. Chen, H. Zhou, Y. Liu, and Y. Chen. “Mobile GPU Power Consumption Reduction via Dynamic Resolution and Frame Rate Scaling,” in Proceedings of the International Workshop on Power-Aware Computing and System, No. 5, Oct. 2014.
• [USENIX HotMobile ‘13] ^◦ X. Chen, Y. Chen, Z. Ma, and F. Fernandes. “How is Energy Consumed in Smartphone Display Applications?” in Proceedings of the International Workshop on Mobile Computing Systems and Applications, No. 3, Feb. 2013.

Conference Posters and Tracks of Work-in-Progress

• [DAC-WIP ‘23] ^† C. Xu, F. Yu, Z. Xu, C. Liu, J. Xiong, and X. Chen. “QuadraNet: Improving High-Order Neural Interaction Efficiency with Hardware-Aware Quadratic Neural Networks,” the Design Automation Conference, Work-in-Progress Workshop, Jul. 2023.
• [MLSys-CrossFL ‘22] ^† Y. Yu, F. Yu, Z. Xu, D. Wang, M. Zhang, A. Li, S. Bray, C. Liu, and X. Chen. “Powering Multi-Task Federated Learning with Competitive GPU Resource Sharing,” the Conference on Machine Learning and Systems, Workshop on Cross-Community Federated Learning: Algorithms, Systems and Co-designs, Poster, Sep. 2022.
• [DAC-WIP ‘22] ^† Y. Yu, F. Yu, Z. Xu, D. Wang, M. Zhang, A. Li, S. Bray, C. Liu, and X. Chen. “Rethinking Multi-Tenant AI with GPU Computing,” the Design Automation Conference, Work-in-Progress Workshop, Jul. 2022.
• [DAC-WIP ‘22] ^* S. Bray, Z. Xu, X. Chen, and C. Liu. “An In-Sensor Adversarial Attacks Defending Approach with Computing-in-Memory Engine,” the Design Automation Conference, Work-in-Progress Workshop, Jul. 2022.
• [DAC-WIP ‘21] ^† F. Yu, Z. Xu, D. Wang, C. Liu, and X. Chen. “DeltaNet: High-Performance Federated Learning with Hybrid Data and Model Parallelism,” the Design Automation Conference, Work-in-Progress Workshop, Dec. 2021.
• [IBM-AICS ‘20] ^† Z. Xu, J. Xiong, F. Yu, and X. Chen. “Efficient Neural Network Implementation with Quadratic Neuron,” the IBM IEEE CAS/EDS AI Compute Symposium, Poster, Nov. 2020.
• [IBM-AICS ‘19] ^† Z. Xu, F. Yu, C. Liu, and X. Chen. “A Comprehensive and Lightweight CNN Defense Methodology against Physical Adversarial Attacks on Embedded Multimedia Applications,” the IBM IEEE CAS/EDS AI Compute Sym., Poster, Oct. 2019.
• [SEC-EdgeSP ‘18] ^† Z. Qin, F. Yu, C. Liu, Y. Wang, and X. Chen. “Adge: An ADMM-Based Audio Adversarial Example Generation Method,” the ACM/IEEE Symposium on Edge Computing, Workshop on Security and Privacy in Edge Computing, Poster, Oct. 2018.
• [DAC-WIP ‘18] ^† F. Yu, Q. Dong, and X. Chen. “ASP: A Fast Adversarial Attack Example Generation Framework based on Adversarial Saliency Prediction,” the Design Automation Conference, Work-in-Progress Workshop, Jun. 2018.
• [DAC-WIP ‘18] ^† Z. Xu, F. Yu, and X. Chen. “Performance-Aware Dynamic Model Generation for Convolutional Neural Network in Mobile Systems,” the Design Automation Conference, Work-in-Progress Workshop, Jun. 2018.
• [DAC-WIP ‘13] ^◦ X. Chen, Z. Ma, F. Fernandes, J. Xue, and Y. Chen. “Dynamic Tone Mapping on OLED Display based on Video Classification,” the Design Automation Conference, Work-in-Progress Workshop, Jun. 2013.
• [DAC-WIP ‘13] ^◦ X. Chen, and H. Li. “P-Spectrum: A Personalized Smartphone Power Management Technique based on Real-time Battery and User Behavior Monitoring,” the Design Automation Conference, Work-in-Progress Workshop, Jun. 2013.
• [DAC-WIP ‘13] K. Nixon, X. Chen, Z. Mao, K. Li, and Y. Chen. “The Invisible Shield: User Classification and Authentication for Mobile Device based on Gesture Recognition,” the Design Automation Conference, Work-in-Progress Workshop, Jun. 2013.

Archived Papers

• [arXiv ‘23] ^† M. Zhang, F. Yu, Y. Yu, M. Zhang, A. Li, and X. Chen. “FedHC: A Scalable Federated Learning Framework for Heterogeneous and Resource-Constrained Clients,” arXiv:2305.15668, May 2023.
• [arXiv ‘23] ^† Y. Yu, F. Yu, M. Zhang, D. Wang, T. Soyata, C. Liu, and X. Chen. “GACER: Granularity-Aware ConcurrEncy Regulation for Multi-Tenant Deep Learning,” arXiv:2304.11745, Apr. 2023.
• [arXiv ‘22] ^† F Yu, D Wang, L Shangguan, M Zhang, C Liu, and X Chen. “A Survey of Multi-Tenant Deep Learning Inference on GPU,” arXiv:2203.09040, Mar. 2022.
• [arXiv ‘21] ^† F. Yu, D. Wang, L. Shangguan, M. Zhang, X. Tang, C. Liu, and X. Chen. “A Survey of Large-Scale Deep Learning Serving System Optimization: Challenges and Opportunities,” arXiv:2111.14247, Nov. 2021.
• [arXiv ‘20] ^† F. Yu, Z. Xu, T. Shen, D. Stamoulis, L. Shangguan, D. Wang, R. Madhok, C. Zhao, X. Li, N. Karianakis, D. Lymberopoulos, A. Li, C. Liu, Y. Chen, and X. Chen. “Towards Latency-aware DNN Optimization with GPU Runtime Analysis and Tail Effect Elimination,” arXiv:2011.03897, Nov. 2020.
• [arXiv ‘20] ^† F. Yu, W. Zhang, Z. Qin, Z. Xu, D. Wang, C. Liu, Z. Tian, and X. Chen. “Heterogeneous Federated Learning,” arXiv:2008.06767, Aug. 2020.
• [arXiv ‘18] S. Ye, T. Zhang, K. Zhang, J. Li, K. Xu, Y. Yang, F. Yu, J. Tang, M. Fardad, S. Liu, X. Chen, X. Lin, and Y. Wang. “Progressive Weight Pruning of Deep Neural Networks using ADMM,” arXiv:1810.07378, Oct. 2018.
• [arXiv ‘18] ^† F. Yu, C. Liu, Y. Wang, L. Zhao, and X. Chen. “Interpreting Adversarial Robustness: A View from Decision Surface in Input Space,” arXiv:1810.00144, Oct. 2018.
• [arXiv ‘18] ^† Z. Xu, F. Yu, C. Liu, and X. Chen. “HASP: A High-Performance Adaptive Mobile Security Enhancement Against Malicious Speech Recognition,” arXiv:1809.01697, Sep. 2018.
• [arXiv ‘18] ^† F. Yu, Z. Xu, Y. Wang, C. Liu, and X. Chen. “Towards Robust Training of Neural Networks by Regularizing Adversarial Gradients,” arXiv:1805.09370, May 2018.

Book Chapters

• ^◦ X. Chen, Z. Xu, and F. Yu. “Mobile Computer Framework for Federated Learning,” Federated Learning: Theory and Practice, Springer, 2022, in press.
• C. Liu, X. Chen. “Enabling Neuromorphic Computing for Artificial Intelligence with Hardware-Software Co-Design ,” Neuromorphic Computing, IntechOpen, 2023, in press.