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Tiny silicon structures compute with heat, achieving 99% accurate matrix multiplication
MIT researchers have designed silicon structures that can perform calculations in an electronic device using excess heat instead of electricity. These tiny structures could someday enable more ...
Abstract: For many scientific applications, dense matrix multiplication is one of the most important and computation intensive linear algebra operations. An efficient matrix multiplication on high ...
Sparse matrix-matrix multiplication (SpMM) is a crucial kernel in various applications, including sparse deep neural networks [1]–[6], graph analytics [7], triangle counting [8], and linear algebra ...
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