News
[05/2024] One collaborative work on Out-of-Distribution Detection is accepted in ICML'24!
[11/2023] One collaborative work on the High-Order Neural Network Design received the Best Paper Award Nomination in ASP-DAC'24!
[09/2022] One collaborative work on Multi-Tenant Federated Learning received the Best Paper Award in MLSys CrossFL Workshop'22 (Link)!
[05/2022] One collaborative work on Quadratic Neural Networks received the Outstanding Paper Award in MLSys'22 (Link)!
[04/2022] I received the Outstanding Academic Achievement Award 2022 of GMU Volgenau School of Engineering (Link)!
[04/2022] One work on memory optimization for recommendation model (during Facebook Internship) is accepted in ICDCS'22 !
[04/2022] Our work on GPU-aware DNN design (poster) is accepted in EuroSys'22 !
[07/2021] Our work on multi-tenant DNN scheduling on GPUs is accepted in ICCAD'21 !
[05/2021] Our work on feature-aligned federated learning (Fed^2) is accepted in KDD'21 !
[02/2021] I will intern at Facebook (Infrastructure Team, Capacity Engineering & Analysis) in summer, 2021 !
[11/2020] One project (Privacy-preserving FL with personal mobility data) won the first prize in IEEE Services Hackathon 2020 [link] !
[04/2020] Our work ''Antidote'' on dynamic feature pruning receives the Best Paper Award Nomination in DATE'20 !
[01/2020] I will intern at Microsoft Research (Redmond) under the supervision of Dr. Di Wang in summer, 2020 !
[12/2019] It's my great pleasure to give a talk as a guest speaker at the Embedded AI Summit 2019 (Shenzhen, China) [link] !
[05/2019] Our work "Interpreting and Evaluating Adversarial Robustness" is accepted in IJCAI'19 !
[03/2019] I will intern at Microsoft Research (Redmond) under the supervision of Dr. Di Wang in summer, 2019 !
[03/2019] One work on Gradient-Free DNN Training using ADMM by Junxiang and me is accepted in KDD'19 !
Research Area
High-Performance Deep Learning Systems
Full-Stack Optimization on GPUs ( Algorithm/Compiler/Hardware Runtime )
Recommendation model memory system optimization
Interpretable and Explainable Artificial Intelligence
Visualizing and Understanding DNN Functionality
Interpreting Neural Network Loss Landscape and Geometry Properties
Deep Learning Generalization and Robustness
Unsupervised Domain Adaptation
Adversarial Examples in Deep Learning Systems
Neural Network Robustness Analysis and Evaluation
Publications
2024
[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 International Conference on Machine Learning (ICML), 2024.
[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, accepted in the Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 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 29th Asia and South Pacific Design Automation Conference (Best Paper Award Nomination), 2024.
2023
[NeurIPS Workshop'23] Y. Jian, F. Yu, S. Singh, D. Stamoulis. Stable Diffusion For Aerial Object Detection, in NeurIPS 2023 Workshop on Synthetic Data Generation with Generative AI (NeurIPS Synthetic4ML), Dec. 2023.
[DAC'23] Y. Yu, F. Yu, X. Sheng, C. Liu, and X. Chen. EagleRec: Edge-Scale Recommendation System Acceleration with Inter-Stage Parallelism Optimization on GPUs, in Proceedings of the 56th Design Automation Conference (DAC), Sep. 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, in submission, 2023.
[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, in submission, 2023.
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 the Fifth Conference on Machine Learning and Systems (MLSys), Sep. 2022 (Outstanding Paper Award).
[MLSys-CrossFL'22] Y. Yu, F. Yu, Z. Xu, X. Chen. Powering Multi-Task Federated Learning with Competitive GPU Resource Sharing, accepted in the CrossFL Workshop co-located with MLSys, Sep. 2022 (Best Poster Award).
[MLSys-AIOps'22] F. Yu, Y. Yu, D. Wang, M. Zhang, L. Shangguan, C. Liu, T. Soyata, X. Chen. A Survey of Multi-Tenant Deep Learning Inference on GPU, accepted in the Cloud Intelligence / AIOps Workshop co-located with MLSys, Sep. 2022.
[EuroSys'22 Poster] F. Yu, Z. Xu, T. Shen, D. Stamoulis, L. Shangguan, D. Wang, M. Zhang, X. Tong, R. Madhok, C. Zhao, X. Li, N. Karianakis, D. Lymberopoulos, C. Liu, A. Li, Y. Chen, and X. Chen. Rethinking Latency-aware DNN Design with GPU Tail Effect Analysis, poster accepted in the 17th European Conference on Computer Systems (EuroSys), Apr 2022.
[ICDCS'22] EK Ardestani, C Kim, SJ Lee, L Pan, V Rampersad, J Axboe, B Agrawal, F. Yu, A. Yu, T. Le, H. Yuen, S. Juluri, A. Nanda, M. Wodekar, D. Mudigere, K. Nair, M. Naumov, C. Peterson, M. Smelyanskiy, V. Rao. Supporting Massive DLRM Inference Through Software Defined Memory, in the 42nd IEEE International Conference on Distributed Computing Systems (ICDCS), Jul. 2022.
[TCAD'22] F. Yu, Z. Xu, C. Liu, D. Stamoulis, D. Wang, Y. Wang, and X. Chen. AntiDoteX: Attention-based Dynamic Optimization for Neural Network Runtime Efficiency, in the Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), June 2022.
[WACV '22] F. Yu, D. Wang, Y. Chen, Nikos. Karianakis, P. Yu, D. Lymberopoulos, S. Lu, W. Shi, 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 (WACV), Jan. 2022.
[WACV '22] Z. Xu, F. Yu, Z. Wu, H. Wang, and X. Chen. FalCon: Fine-grained Feature Map Sparsity Computing with Decomposed Convolutions for Inference Optimization, in Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), Jan. 2022.
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 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Aug. 2021.
[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 40th IEEE International Conference on Computer-Aided Design (ICCAD), Nov. 2021.
[DAC'21] Z. Xu, F. Yu, J. Xiong, X. Chen. Helios: Heterogeneity-Aware Federated Learning with Dynamically Balanced Collaboration, in Proceedings of the 58th Design Automation Conference (DAC), Dec. 2021.
[TCAD'21] Z. Qin, F. Yu, Z. Xu, C. Liu, X. Chen. CaptorX: A Class-Adaptive Convolutional Neural Network Reconfiguration Framework, in the Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), May 2021.
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 the 23rd Design Automation and Test in Europe Conference (Best Paper Award Nomination), Mar. 2020.
[DATE'20] F. Yu, Z. Qin, D. Wang, P. Xu, C. Liu, T. Zhi, and X. Chen. DC-CNN: Computational Flow Redefinition for Efficient CNN Inference through Model Structural Decoupling, in the 23rd Design Automation and Test in Europe Conference (DATE), Mar. 2020.
[TCAD'20] F. Yu, Z. Qin, C. Liu, D. Wang, X. Chen. REIN the RobuTS: Robust DNN-based Image Recognition in Autonomous Driving Systems, in the Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), Sep. 2020.
[TCAD'20] Z. Xu, F. Yu, Z. Qin, C. Liu, X. Chen. DirectX: Dynamic Resource-Aware CNN Reconfiguration Framework for Real-Time Mobile Applications, in the Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), May 2020.
[CVPR Workshop'20] F. Yu, D. Wang, Y. Chen, Nikos. Karianakis, P. Yu, D. Lymberopoulos, X. Chen, Unsupervised Domain Adaptation for Object Detection via Cross-Domain Semi-Supervised Learning (CVPR V4AS Workshop), Jun. 2020.
[ASP-DAC'20] X. Ma, G. Yuan, S. Lin, C. Ding, F. Yu, T. Liu, W. Wen, X. Chen, Y. Wang. Tiny but Accurate: A Pruned, Quantized and Optimized Memristor Crossbar Framework for Ultra-Efficient DNN Implementation, in Proc. of the 25th Asia and South Pacific Design Automation Conference, Jan. 2020.
2019
[IJCAI'19] F. Yu, Z. Qin, C. Liu, L. Zhao, Y. Wang, X. Chen. Interpreting and Evaluating Adversarial Robustness, in Proceedings of the 28th International Joint Conference of Artificial Intelligence (IJCAI), Aug. 2019.
[KDD'19] J. Wang, F. Yu, X. Chen, L. Zhao. ADMM for Efficient Deep Learning with Global Convergence, in Proceedings of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Aug. 2019.
[DAC '19] F. Yu*, Z. Xu*, C. Liu, and X. Chen. MASKER: Adaptive Mobile Security Enhancement against Automatic Speech Recognition in Eavesdropping. in Proceedings of the 56th Design Automation Conference (DAC), Jun. 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 56th Design Automation Conference (DAC), Jun. 2019.
[BMVC'19] Z. Qin, F. Yu, C. Liu, X. Chen. Functionality-Oriented Convolutional Filter Pruning, in the Proceedings of 30th British Machine Vision Conference (BMVC), Sep. 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 24th Asia and South Pacific Design Automation Conference (ASP-DAC), Jan. 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 24th Asia and South Pacific Design Automation Conference (ASP-DAC), Jan. 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 24th Asia and South Pacific Design Automation Conference (ASP-DAC), Jan. 2019.
2018
[NIPS Workshop'18] F. Yu, Z. Qin, and X. Chen. Distilling Critical Paths in Convolutional Neural Networks. in the 32nd Conference on Neural Information Processing Systems, Workshop on Compact Deep Neural Networks with industrial applications, Dec. 2018.
[NIPS Workshop'18] Z. Qin, F. Yu, C. Liu, and X. Chen. Demystifying Neural Network Filter Pruning. in the 32nd Conference on Neural Information Processing Systems, Workshop on Compact Deep Neural Networks with industrial applications, Dec. 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 23rd ACM/IEEE International Symposium on Low Power Electronics and Design, No. 37, pp. 1 6, Jul. 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 17th IEEE Computer Society Annual Symposium on VLSI, pp. 470 475, Jul. 2018.
[DAC WIP'18] F. Yu, Q. Dong, and X. Chen. ASP: A Fast Adversarial Attack Example Generation Framework based on Adversarial Saliency Prediction. in the 55th Design Automation Conference, Working In Progress, Jun. 2018.
[MFC '18] Z. Qin, F. Yu, C. Liu, X. Chen. How convolutional neural networks see the world - A survey of convolutional neural network visualization methods. in the Journal of Mathematical Foundations of Computing, pp.149 180, May. 2018.