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Some of the most promising among these are Ising machines (IMs), physics-based systems designed to tackle complex optimization problems. In recent years, engineers have been trying to devise new computers and devices that could help to solve challenging real-world problems faster and more efficiently. In 2019, Camsari and his colleagues showed that eight p-bit networks based on nanodevices could help solve some hard optimization problems in energy-efficient ways. In the future, the sparse Ising machine architecture developed by Camsari, Finocchio, Aadit, Grimaldi and their colleagues could be applied to several other real-world optimization problems. Researchers at the University of California and the University of Messina have recently developed a sparse Ising machine architecture that can operate on classical and existing computer hardware. More information: Anjum Aadit et al, Massively parallel probabilistic computing with sparse Ising machines, Nature Electronics (2022). In fact, they found that their Ising machine could tackle optimization problems as well as, if not better, than many state-of-the-art classical techniques, while running on existing p-computers. Credit: Aadit et al The sparse Ising machine developed by Camsari, Finocchio and their colleagues is based on the idea that when making probabilistic decisions, parallelism comes from sparsity. (Tech Xplore). Continue reading.

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