activity
20232026
collaborators

7 papers

cs.GR2026

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…

cs.CV2026

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…

cs.LG2025

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…

cs.CV2024

Lotus: learning-based online thermal and latency variation management for two-stage detectors on edge devices

Yifan Gong, Yushu Wu, Zheng Zhan +5

Two-stage object detectors exhibit high accuracy and precise localization, especially for identifying small objects that are favorable for various edge applications. However, the h…

cs.DC2024

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…

cs.LG2024

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…

cs.ET2023

SupeRBNN: Randomized Binary Neural Network Using Adiabatic Superconductor Josephson Devices

Zhengang Li, Geng Yuan, Tomoharu Yamauchi +8

Adiabatic Quantum-Flux-Parametron (AQFP) is a superconducting logic with extremely high energy efficiency. By employing the distinct polarity of current to denote logic `0' and `1'…