6 papers
Accelerating 3D Gaussian Splatting using Tensor Cores
Sheng Li, Yang Sui, Yue Wu +4
3D Gaussian Splatting (3DGS) has become a leading technique for real-time neural rendering and 3D scene reconstruction, but its rendering cost remains too high for many latency-sen…
Temporal Aware Pruning for Efficient Diffusion-based Video Generation
Sheng Li, Yang Sui, Junhao Ran +3
Video diffusion models have recently enabled high-quality video generation with ViT-based architectures, but remain computationally intensive because generation requires attention…
DRNet: All-in-One Image Restoration via Prior-Guided Dynamic Reparameterization
Ao Li, Xiaoning Liu, Sheng Li +5
All-in-one image restoration aims to handle diverse degradations within a single model. However, existing methods often suffer from three key limitations: 1) per-input computationa…
Rethinking the Potential of Layer Freezing for Efficient DNN Training
Chence Yang, Ci Zhang, Lei Lu +11
With the growing size of deep neural networks and datasets, the computational costs of training have significantly increased. The layer-freezing technique has recently attracted gr…
Improving GPU Multi-Tenancy Through Dynamic Multi-Instance GPU Reconfiguration
Tianyu Wang, Sheng Li, Bingyao Li +6
Continuous learning (CL) has emerged as one of the most popular deep learning paradigms deployed in modern cloud GPUs. Specifically, CL has the capability to continuously update th…
EdgeOL: Efficient in-situ Online Learning on Edge Devices
Sheng Li, Geng Yuan, Yue Dai +10
Emerging applications, such as robot-assisted eldercare and object recognition, generally employ deep learning neural networks (DNNs) and naturally require: i) handling streaming-i…