Sparse matrix computations are pivotal to advancing high-performance scientific applications, particularly as modern numerical simulations and data analyses demand efficient management of large, ...

DGIST announced on July 4 that Professor Min-Soo Kim's team in the Department of Information and Communication Engineering developed the DistME (Distributed Matrix Engine) technology that can analyze ...

Researchers have developed an easy-to-use optical chip that can configure itself to achieve various functions. The positive real-valued matrix computation they have achieved gives the chip the ...

MIT engineers use heat-conducting silicon microstructures to perform matrix multiplication with >99% accuracy hinting at ...

Most traditional high-performance computing applications focus on computations on very large matrices. Think seismic analysis, weather prediction, structural analysis. But today, with advances in deep ...

MIT researchers have designed silicon structures that can perform calculations in an electronic device using excess heat ...

Chinese researchers have made a significant breakthrough in the field of computing by developing a high-precision scalable analog matrix computing chip. This new analog chip is touted to be 1,000 ...

Matrix multiplication is at the heart of many scientific applications and has been optimized to run on both the host Intel Xeon CPUs as well as the Intel Xeon Phi coprocessors. Matrix multiplies can ...

This journal, begun in 1943 as Mathematical Tables and Other Aids to Computation, publishes original articles on all aspects of numerical mathematics, book reviews, mathematical tables, and technical ...

Scientists in the US have created a tiny silicon chip that can perform mathematical ...