2024
SparseACC: A Generalized Linear Model Accelerator for Sparse Datasets.
IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., March, 2024
RPCAcc: A High-Performance and Reconfigurable PCIe-attached RPC Accelerator.
CoRR, 2024
Understanding Routable PCIe Performance for Composable Infrastructures.
Proceedings of the 21st USENIX Symposium on Networked Systems Design and Implementation, 2024
Feature Transformation for Few-Shot Learning.
Proceedings of the International Joint Conference on Neural Networks, 2024
Demystifying Datapath Accelerator Enhanced Off-path SmartNIC.
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Proceedings of the 32nd IEEE International Conference on Network Protocols, 2024
DmRPC: Disaggregated Memory-aware Datacenter RPC for Data-intensive Applications.
Proceedings of the 40th IEEE International Conference on Data Engineering, 2024
2023
Federated mutual learning: a collaborative machine learning method for heterogeneous data, models, and objectives.
Frontiers Inf. Technol. Electron. Eng., October, 2023
P4SGD: Programmable Switch Enhanced Model-Parallel Training on Generalized Linear Models on Distributed FPGAs.
IEEE Trans. Parallel Distributed Syst., August, 2023
Helios: An Efficient Out-of-core GNN Training System on Terabyte-scale Graphs with In-memory Performance.
CoRR, 2023
Federated Generative Learning with Foundation Models.
CoRR, 2023
Addressing Catastrophic Forgetting in Federated Class-Continual Learning.
CoRR, 2023
Legion: Automatically Pushing the Envelope of Multi-GPU System for Billion-Scale GNN Training.
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Proceedings of the 2023 USENIX Annual Technical Conference, 2023
SmartDS: Middle-Tier-centric SmartNIC Enabling Application-aware Message Split for Disaggregated Block Storage.
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Proceedings of the 50th Annual International Symposium on Computer Architecture, 2023
Federated Domain Adaptation via Pseudo-label Refinement.
Proceedings of the IEEE International Conference on Multimedia and Expo, 2023
IDEAL: Query-Efficient Data-Free Learning from Black-Box Models.
Proceedings of the Eleventh International Conference on Learning Representations, 2023
Rethinking Data Distillation: Do Not Overlook Calibration.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023
TARGET: Federated Class-Continual Learning via Exemplar-Free Distillation.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023
Accelerating Dataset Distillation via Model Augmentation.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023
Delving into the Adversarial Robustness of Federated Learning.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023
2022
Shuhai: A Tool for Benchmarking High Bandwidth Memory on FPGAs.
IEEE Trans. Computers, 2022
QEKD: Query-Efficient and Data-Free Knowledge Distillation from Black-box Models.
CoRR, 2022
FpgaNIC: An FPGA-based Versatile 100Gb SmartNIC for GPUs.
Proceedings of the 2022 USENIX Annual Technical Conference, 2022
DENSE: Data-Free One-Shot Federated Learning.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Federated Learning with Label Distribution Skew via Logits Calibration.
Proceedings of the International Conference on Machine Learning, 2022
Adversarial Examples for Good: Adversarial Examples Guided Imbalanced Learning.
Proceedings of the 2022 IEEE International Conference on Image Processing, 2022
Towards Efficient Data Free Blackbox Adversarial Attack.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022
2021
A Practical Data-Free Approach to One-shot Federated Learning with Heterogeneity.
CoRR, 2021
2020
Federated Mutual Learning.
CoRR, 2020
Benchmarking High Bandwidth Memory on FPGAs.
CoRR, 2020
Shuhai: Benchmarking High Bandwidth Memory On FPGAS.
Proceedings of the 28th IEEE Annual International Symposium on Field-Programmable Custom Computing Machines, 2020