Sparse matrix computations are pivotal to advancing high-performance scientific applications, particularly as modern numerical simulations and data analyses demand efficient management of large, ...
A novel AI-acceleration paper presents a method to optimize sparse matrix multiplication for machine learning models, particularly focusing on structured sparsity. Structured sparsity involves a ...
With $100 million in “patience money” from a few individual investors who believe in the future of sparse matrix-based computing on low-power and reprogrammable devices, Austin-based Knupath, has ...
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