Tensor network methods provide a structured approach to representing and manipulating high-dimensional data by decomposing global information into interconnected low-rank tensors. Originating in the ...

Deep machine learning has achieved remarkable success in various fields of artificial intelligence, but achieving both high interpretability and high efficiency simultaneously remains a critical ...

Researchers from The University of New Mexico and Los Alamos National Laboratory have developed a novel computational framework that addresses a longstanding challenge in statistical physics. The ...

New computational techniques, 'HighLight' and 'Tailors and Swiftiles,' could dramatically boost the speed and performance of high-performance computing applications like graph analytics or generative ...

TPUs are Google’s specialized ASICs built exclusively for accelerating tensor-heavy matrix multiplication used in deep learning models. TPUs use vast parallelism and matrix multiply units (MXUs) to ...