4 citations · 9 across the 6 of their papers we have counts for
6 papers
YOLO-DS: Fine-Grained Feature Decoupling via Dual-Statistic Synergy Operator for Object Detection
Lin Huang, Yujuan Tan, Weisheng Li +6
One-stage object detection, particularly the YOLO series, strikes a favorable balance between accuracy and efficiency. However, existing YOLO detectors lack explicit modeling of he…
Reuse Kernels or Activations? A Flexible Dataflow for Low-latency Spectral CNN Acceleration
Yue Niu, Rajgopal Kannan, Ajitesh Srivastava +1
Spectral-domain CNNs have been shown to be more efficient than traditional spatial CNNs in terms of reducing computation complexity. However they come with a `kernel explosion' pro…
mL-BFGS: A Momentum-based L-BFGS for Distributed Large-Scale Neural Network Optimization
Yue Niu, Zalan Fabian, Sunwoo Lee +2
Quasi-Newton methods still face significant challenges in training large-scale neural networks due to additional compute costs in the Hessian related computations and instability i…
Federated Learning of Large Models at the Edge via Principal Sub-Model Training
Yue Niu, Saurav Prakash, Souvik Kundu +2
Federated Learning (FL) is emerging as a popular, promising decentralized learning framework that enables collaborative training among clients, with no need to share private data b…
3LegRace: Privacy-Preserving DNN Training over TEEs and GPUs
Yue Niu, Ramy E. Ali, Salman Avestimehr
Leveraging parallel hardware (e.g. GPUs) for deep neural network (DNN) training brings high computing performance. However, it raises data privacy concerns as GPUs lack a trusted e…
SPEC2: SPECtral SParsE CNN Accelerator on FPGAs
Yue Niu, Hanqing Zeng, Ajitesh Srivastava +4
To accelerate inference of Convolutional Neural Networks (CNNs), various techniques have been proposed to reduce computation redundancy. Converting convolutional layers into freque…