11 citations · 11 across the 31 of their papers we have counts for
34 papers
ByteSplat: Efficient Distributed 3D Gaussian Splatting Training via Intra- and Inter-GPU communication reduction
Shuo Wu, He Zhu, Han Zhao +9
3D Gaussian Splatting (3DGS) enables photorealistic scene reconstruction, but training large-scale scenes requires substantial memory and computation. Distributing training across…
Shared Phase Arithmetic for Parallel Quantum Rotations
Anbang Wu, Liqiang Lu, Pu Pang +3
A layer of diagonal quantum gates can be specified by an integer-valued function on computational basis states. This description suggests implementing the layer by evaluating that…
Atlas: Algorithm-Hardware Co-Design for On-Device City-Scale 3D Gaussian Splatting in VR
He Zhu, Zheng Liu, Xingyang Li +8
3D Gaussian splatting (3DGS) has drawn significant attention in the architectural community recently. However, enabling city scale 3DGS on mobile VR devices remains challenging, as…
A Thread-Register Decoupled GPU Execution Model for Efficient Tensor Computation
Zihan Liu, Jingwen Leng, Yangjie Zhou +12
Modern GPUs increasingly integrate Tensor Cores into the execution pipeline. Although aggregate tensor throughput continues to grow, aided by an operand supply that has evolved fro…
Deltoris: Enabling Real-time VLA Inference in Embodied AI via Bit-level Sparsity and Speculative Inference
Zheng Liu, Zeyu Guo, Zihan Liu +9
Vision-language-action (VLA) models have emerged as a key component in embodied AI. Among existing approaches, diffusion-based VLA models achieve superior motion quality and genera…
DSTAR: Accelerating Diffusion Transformers via Spatial and Temporal Redundancy Reduction
Chi Zhang, Jieru Zhao, Yu Feng +3
Diffusion Transformers (DiTs) have been widely used in many tasks, including image synthesis, video generation, and content editing. However, their multi-iteration inference proces…
Kaleido: Algorithm-Hardware Co-Design for Video Diffusion Transformers by Exploiting Latent Space Correlations
Wenxuan Miao, Haosong Liu, Weiming Hu +9
Video diffusion transformers (vDiTs) generate high quality video but introduce extremely high compute cost due to the long diffusion timesteps and self attention computation. As di…
GF-DiT: Scheduling Parallelism for Diffusion Transformer Serving
Xinwei Qiang, Yifan Hu, Shixuan Sun +6
Diffusion Transformers (DiTs) have become the dominant architecture for image and video generation, creating growing demand for efficient DiT serving. Existing systems assign each…
MoE-Hub: Taming Software Complexity for Seamless MoE Overlap with Hardware-Accelerated Communication on Multi-GPU Systems
Zhuoshan Zhou, Chen Zhang, Shuyi Zhang +10
The Mixture-of-Experts (MoE) architecture is crucial for scaling large language models, but its scalability is severely limited by inter-GPU communication bottlenecks in multi-GPU…
Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs
Qijun Zhang, Chen Zhang, Zhuoshan Zhou +10
Mixture-of-Experts (MoE) has been adopted by many leading large models to reduce computational requirements. However, frequent inter-GPU communication in MoE expert parallelism (EP…
CuBridge: An LLM-Based Framework for Understanding and Reconstructing High-Performance Attention Kernels
Xing Ma, Yangjie Zhou, Wu Sun +6
Efficient CUDA implementations of attention mechanisms are critical to modern deep learning systems, yet supporting diverse and evolving attention variants remains challenging. Exi…
On the (In-)Security of the Shuffling Defense in the Transformer Secure Inference
Zhengyi Li, Yakai Wang, Kang Yang +6
For Transformer models, cryptographically secure inference ensures that the client learns only the final output, while the server learns nothing about the client's input. However,…
CODO: An Automated Compiler for Comprehensive Dataflow Optimization
Weichuang Zhang, Yiquan Wang, Xinzhou Zhang +6
FPGAs are well-suited for dataflow architectures that process data in a streaming or pipelined manner, thus satisfying the high computational and communication demands of emerging…
gMatch: Fine-Grained and Hardware-Efficient Subgraph Matching on GPUs
Weitian Chen, Shixuan Sun, Cheng Chen +3
Subgraph matching is a core operation in graph analytics, supporting a broad spectrum of applications from social network analysis to bioinformatics. Recent GPU-based approaches ac…
InfiniLoRA: Disaggregated Multi-LoRA Serving for Large Language Models
Hongyu Chen, Letian Ruan, Zilin Xu +6
LoRA enables efficient customization of LLMs and is widely used in multi-tenant and multi-task serving. However, emerging model architectures such as MoE significantly increase LoR…
DASH: Deterministic Attention Scheduling for High-throughput Reproducible LLM Training
Xinwei Qiang, Hongmin Chen, Shixuan Sun +3
Determinism is indispensable for reproducibility in large language model (LLM) training, yet it often exacts a steep performance cost. In widely used attention implementations such…
Yggdrasil: Bridging Dynamic Speculation and Static Runtime for Latency-Optimal Tree-Based LLM Decoding
Yue Guan, Changming Yu, Shihan Fang +8
Speculative decoding improves LLM inference by generating and verifying multiple tokens in parallel, but existing systems suffer from suboptimal performance due to a mismatch betwe…
FlashFuser: Expanding the Scale of Kernel Fusion for Compute-Intensive Operators via Inter-Core Connection
Ziyu Huang, Yangjie Zhou, Zihan Liu +8
The scaling of computation throughput continues to outpace improvements in memory bandwidth, making many deep learning workloads memory-bound. Kernel fusion is a key technique to a…
Splatonic: Architecture Support for 3D Gaussian Splatting SLAM via Sparse Processing
Xiaotong Huang, He Zhu, Tianrui Ma +8
3D Gaussian splatting (3DGS) has emerged as a promising direction for SLAM due to its high-fidelity reconstruction and rapid convergence. However, 3DGS-SLAM algorithms remain impra…
ClusterFusion: Expanding Operator Fusion Scope for LLM Inference via Cluster-Level Collective Primitive
Xinhao Luo, Zihan Liu, Yangjie Zhou +8
Large language model (LLM) decoding suffers from high latency due to fragmented execution across operators and heavy reliance on off-chip memory for data exchange and reduction. Th…
SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity
Xingyang Li, Jie Jiang, Yu Feng +5
Rendering is critical in fields like 3D modeling, AR/VR, and autonomous driving, where high-quality, real-time output is essential. Point-based neural rendering (PBNR) offers a pho…
RapidStore: An Efficient Dynamic Graph Storage System for Concurrent Queries
Chiyu Hao, Jixian Su, Shixuan Sun +7
Dynamic graph storage systems are essential for real-time applications such as social networks and recommendation, where graph data continuously evolves. However, they face signifi…
STREAMINGGS: Voxel-Based Streaming 3D Gaussian Splatting with Memory Optimization and Architectural Support
Chenqi Zhang, Yu Feng, Jieru Zhao +4
3D Gaussian Splatting (3DGS) has gained popularity for its efficiency and sparse Gaussian-based representation. However, 3DGS struggles to meet the real-time requirement of 90 fram…
Lumina: Real-Time Mobile Neural Rendering by Exploiting Computational Redundancy
Yu Feng, Weikai Lin, Yuge Cheng +6
3D Gaussian Splatting (3DGS) has vastly advanced the pace of neural rendering, but it remains computationally demanding on today's mobile SoCs. To address this challenge, we propos…