Modern artificial intelligence systems rely on deep neural networks (DNNs) that demand immense computational resources.

Math module for the PAZ Engine. Just another linear algebra library with quaternions and basic RNG. Complicated stuff is offloaded to Eigen internally. No external dependencies. Like all PAZ Engine ...

There are some details about this implementation: Three by three matrixes are used. Each matrix input is a two byte container, so the maximum value (in decimal) it can hold is 65,535.

A photonic chip uses light to run a deep neural network on a chip, enabling fast processors that can learn instantly.

The deep neural network models that power today's most demanding machine-learning applications have grown so large and ...

One of the standout features of nvmath-python is its ability to fuse epilog operations with matrix multiplication. Epilogs are operations that can be integrated with mathematical computations such as ...

In 2017, MIT researchers succeeded in creating a chip that can run neural network calculations, but there were some notable ...

RISC-V chip designer Tenstorrent has won $693 million of investment – an endorsement of its plans to use the permissively ...

which utilizes a triple row activation scheme to efficiently handle sparse matrix-vector multiplication (SpMV). We found that reducing operations by sparsity relies heavily on how matrices are mapped ...

Physicists from the University of the Witwatersrand (Wits) have developed an innovative computing system using laser beams ...

Verifying an implementation of a novel modular inversion algorithm using a proof assistant. How guaranteeing Bitcoin ...