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 ...
The new method accelerates encrypted matrix multiplication, advancing practical fully homomorphic encryption (FHE) for AI SEOUL, South Korea, Nov. 12, 2025 ...
Abstract: In this paper, we report on the development of an efficient GPU implementation of the Strassen-Winograd matrix multiplication algorithm for matrices of arbitrary sizes. We utilize ...
In 1971, German mathematicians Schönhage and Strassen predicted a faster algorithm for multiplying large numbers, but it remained unproven for decades. Mathematicians from Australia and France have ...
Abstract: General sparse matrix-matrix multiplication (SpGEMM) is a fundamental computational method with wide-ranging applications in scientific simulations, machine learning, and image processing.
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Discovering faster algorithms for matrix multiplication remains a key pursuit in computer science and numerical linear algebra. Since the pioneering contributions of Strassen and Winograd in the late ...
Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of computing a matrix inverse using the Newton iteration algorithm. Compared to other algorithms, Newton ...
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