AI infrastructure startup Tensordyne has taped out its first commercial accelerator, with fabrication on TSMC's 3nm process ...

Transformations are the key to such codes, and they rely on math that predates computing as we know it by centuries. There ...

Triton is a language and compiler for writing highly efficient ML primitives, one of the most common primitive is matrix-multiplication. Triton typically builds these primitives using just-in-time ...

Optical computing has been limited to vector–matrix multiplications, with matrix–matrix operations requiring wavelength- or time-division multiplexing, reducing energy efficiency and speed. Now, ...

CUDA-L2 is a system that combines large language models (LLMs) and reinforcement learning (RL) to automatically optimize Half-precision General Matrix Multiply (HGEMM) CUDA kernels. CUDA-L2 ...

Matrix multiplication runs the modern world. For every word that ChatGPT writes, I estimate that ~10,000,000,000 (10 billion) small matrices must be multiplied. Modern gaming engines routinely draw 10 ...

Abstract: By combining the echoes of two channels in azimuth, and utilizing the super-resolution algorithm for ill-posed problems, the left/right ambiguity in forward-looking synthetic aperture radar ...

An NPU is a dedicated hardware accelerator designed to perform AI operations much more efficiently and faster than CPUs and GPUs. NPU cores are specifically designed to perform matrix multiplication ...

Computer scientists have discovered a new way to multiply large matrices faster than ever before by eliminating a previously unknown inefficiency, reports Quanta Magazine. This could eventually ...

Abstract: Matrix operations are widely used in practical engineering, but the traditional processing methods rely on the loop iterations and neural network algorithm on the software, requiring a long ...