" return np.vstack((np.hstack((mul(A[:2, :2], B[:2, :2]) + mul(A[:2, 2:], B[2:, :2]), mul(A[:2, :2], B[:2, 2:]) + mul(A[:2, 2:], B[2:, 2:]))),\n", " np.hstack((mul(A ...

Abstract: The rapid expansion of Artificial Intelligence (AI) applications has necessitated the implementation of neural networks for better performance and scalability. Matrix multiplication, the ...

Discover how nvmath-python leverages NVIDIA CUDA-X math libraries for high-performance matrix operations, optimizing deep learning tasks with epilog fusion, as detailed by Szymon Karpiński.

set(SOURCE_FILES main.cpp ../libs/block.cpp ../libs/block.h ../libs/matrix.cpp ..//libs/matrix.h ../libs/logging.cpp ../libs/logging.h ../configuration/config.h) add ...

Computer scientists have discovered a new way to multiply large matrices faster by eliminating a previously unknown inefficiency, leading to the largest improvement in matrix multiplication efficiency ...

The most widely used matrix-matrix multiplication routine is GEMM (GEneral Matrix Multiplication) from the BLAS (Basic Linear Algebra Subroutines) library. And these days it can be found being used in ...

Matrix multiplication is at the heart of many machine learning breakthroughs, and it just got faster—twice. Last week, DeepMind announced it discovered a more efficient way to perform matrix ...

Optical computing uses photons instead of electrons to perform computations, which can significantly increase the speed and energy efficiency of computations by overcoming the inherent limitations of ...

Sparse matrix computations are prevalent in many scientific and technical applications. In many simulation applications, the solving of the sparse matrix-vector multiplication (SpMV) is critical for ...