A new technique from Stanford, Nvidia, and Together AI lets models learn during inference rather than relying on static ...
Researchers at Massachusetts Institute of Technology have demonstrated a surprising new way to compute—by using heat instead of electricity. In a proof-of-concept study published in Physical Review ...
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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 ...
Understanding the benefits of matrix converters for EV chargers and a comparison of different matrix converter topologies.
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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