25 citations · 48 across the 19 of their papers we have counts for
20 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…
TIDE: Efficient and Lossless MoE Diffusion LLM Inference with I/O-aware Expert Offload
Zhiben Chen, Youpeng Zhao, Yang Sui +2
Diffusion Large Language Models (dLLMs) have emerged as a competitive alternative to autoregressive (AR) models, offering better hardware utilization and bidirectional context thro…
ATA: Bridging Implicit Reasoning with Attention-Guided and Action-Guided Inference for Vision-Language Action Models
Cheng Yang, Jianhao Jiao, Lingyi Huang +8
Vision-Language-Action (VLA) models rely on current observations, including images, language instructions, and robot states, to predict actions and complete tasks. While accurate v…
EcoSpa: Efficient Transformer Training with Coupled Sparsity
Jinqi Xiao, Cheng Luo, Lingyi Huang +8
Transformers have become the backbone of modern AI, yet their high computational demands pose critical system challenges. While sparse training offers efficiency gains, existing me…
70% Size, 100% Accuracy: Lossless LLM Compression for Efficient GPU Inference via Dynamic-Length Float (DFloat11)
Tianyi Zhang, Mohsen Hariri, Shaochen Zhong +4
Large-scale AI models, such as Large Language Models (LLMs) and Diffusion Models (DMs), have grown rapidly in size, creating significant challenges for efficient deployment on reso…
TopV: Compatible Token Pruning with Inference Time Optimization for Fast and Low-Memory Multimodal Vision Language Model
Cheng Yang, Yang Sui, Jinqi Xiao +8
Vision-Language Models (VLMs) demand substantial computational resources during inference, largely due to the extensive visual input tokens for representing visual information. Pre…
Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models
Yang Sui, Yu-Neng Chuang, Guanchu Wang +9
Large Language Models (LLMs) have demonstrated remarkable capabilities in complex tasks. Recent advancements in Large Reasoning Models (LRMs), such as OpenAI o1 and DeepSeek-R1, ha…
Confident or Seek Stronger: Exploring Uncertainty-Based On-device LLM Routing From Benchmarking to Generalization
Yu-Neng Chuang, Leisheng Yu, Guanchu Wang +6
Large language models (LLMs) are increasingly deployed and democratized on edge devices. To improve the efficiency of on-device deployment, small language models (SLMs) are often a…
MoE-I: Compressing Mixture of Experts Models through Inter-Expert Pruning and Intra-Expert Low-Rank Decomposition
Cheng Yang, Yang Sui, Jinqi Xiao +7
The emergence of Mixture of Experts (MoE) LLMs has significantly advanced the development of language models. Compared to traditional LLMs, MoE LLMs outperform traditional LLMs by…
BitsFusion: 1.99 bits Weight Quantization of Diffusion Model
Yang Sui, Yanyu Li, Anil Kag +7
Diffusion-based image generation models have achieved great success in recent years by showing the capability of synthesizing high-quality content. However, these models contain a…
DisDet: Exploring Detectability of Backdoor Attack on Diffusion Models
Yang Sui, Huy Phan, Jinqi Xiao +6
In the exciting generative AI era, the diffusion model has emerged as a very powerful and widely adopted content generation and editing tool for various data modalities, making the…
ELRT: Efficient Low-Rank Training for Compact Convolutional Neural Networks
Yang Sui, Miao Yin, Yu Gong +3
Low-rank compression, a popular model compression technique that produces compact convolutional neural networks (CNNs) with low rankness, has been well-studied in the literature. O…
In-Sensor Radio Frequency Computing for Energy-Efficient Intelligent Radar
Yang Sui, Minning Zhu, Lingyi Huang +2
Radio Frequency Neural Networks (RFNNs) have demonstrated advantages in realizing intelligent applications across various domains. However, as the model size of deep neural network…
Corner-to-Center Long-range Context Model for Efficient Learned Image Compression
Yang Sui, Ding Ding, Xiang Pan +4
In the framework of learned image compression, the context model plays a pivotal role in capturing the dependencies among latent representations. To reduce the decoding time result…
Reconstruction Distortion of Learned Image Compression with Imperceptible Perturbations
Yang Sui, Zhuohang Li, Ding Ding +4
Learned Image Compression (LIC) has recently become the trending technique for image transmission due to its notable performance. Despite its popularity, the robustness of LIC with…
HALOC: Hardware-Aware Automatic Low-Rank Compression for Compact Neural Networks
Jinqi Xiao, Chengming Zhang, Yu Gong +5
Low-rank compression is an important model compression strategy for obtaining compact neural network models. In general, because the rank values directly determine the model comple…
CSTAR: Towards Compact and STructured Deep Neural Networks with Adversarial Robustness
Huy Phan, Miao Yin, Yang Sui +2
Model compression and model defense for deep neural networks (DNNs) have been extensively and individually studied. Considering the co-importance of model compactness and robustnes…
CHIP: CHannel Independence-based Pruning for Compact Neural Networks
Yang Sui, Miao Yin, Yi Xie +3
Filter pruning has been widely used for neural network compression because of its enabled practical acceleration. To date, most of the existing filter pruning works explore the imp…
Towards Efficient Tensor Decomposition-Based DNN Model Compression with Optimization Framework
Miao Yin, Yang Sui, Siyu Liao +1
Advanced tensor decomposition, such as Tensor train (TT) and Tensor ring (TR), has been widely studied for deep neural network (DNN) model compression, especially for recurrent neu…