1 citations · 1 across the 5 of their papers we have counts for
5 papers
Splaxel: Efficient Distributed Training of 3D Gaussian Splatting for Large-scale Scene Reconstruction via Pixel-level Communication
Wenqi Jia, Zhewen Hu, Ying Huang +10
3D Gaussian Splatting (3DGS) enables high-fidelity and real-time 3D scene reconstruction, but scaling training to large-scale scenes requires optimizing hundreds of millions of Gau…
FTTN: Feature-Targeted Testing for Numerical Properties of NVIDIA & AMD Matrix Accelerators
Xinyi Li, Ang Li, Bo Fang +3
NVIDIA Tensor Cores and AMD Matrix Cores (together called Matrix Accelerators) are of growing interest in high-performance computing and machine learning owing to their high perfor…
A GPU accelerated mixed-precision Smoothed Particle Hydrodynamics framework with cell-based relative coordinates
Zirui Mao, Xinyi Li, Shenyang Hu +2
Smoothed Particle Hydrodynamics (SPH) is essential for modeling complex large-deformation problems across various applications, requiring significant computational power. A major p…
MPGemmFI: A Fault Injection Technique for Mixed Precision GEMM in ML Applications
Bo Fang, Xinyi Li, Harvey Dam +9
Emerging deep learning workloads urgently need fast general matrix multiplication (GEMM). To meet such demand, one of the critical features of machine-learning-specific accelerator…
Evaluating Emerging AI/ML Accelerators: IPU, RDU, and NVIDIA/AMD GPUs
Hongwu Peng, Caiwen Ding, Tong Geng +3
The relentless advancement of artificial intelligence (AI) and machine learning (ML) applications necessitates the development of specialized hardware accelerators capable of handl…