5 citations · 7 across the 5 of their papers we have counts for
5 papers
An SMT Formalization of Mixed-Precision Matrix Multiplication: Modeling Three Generations of Tensor Cores
Benjamin Valpey, Xinyi Li, Sreepathi Pai +1
Many recent computational accelerators provide non-standard (e.g., reduced precision) arithmetic operations to enhance performance for floating-point matrix multiplication. Unfortu…
What Operations can be Performed Directly on Compressed Arrays, and with What Error?
Tripti Agarwal, Harvey Dam, Dorra Ben Khalifa +3
In response to the rapidly escalating costs of computing with large matrices and tensors caused by data movement, several lossy compression methods have been developed to significa…
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…