A team of researchers developed “parallel optical matrix-matrix multiplication” (POMMM), which could revolutionize tensor ...
MIT engineers use heat-conducting silicon microstructures to perform matrix multiplication with >99% accuracy hinting at energy-efficient analog computing beyond traditional chips.
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TPUs are Google’s specialized ASICs built exclusively for accelerating tensor-heavy matrix multiplication used in deep learning models. TPUs use vast parallelism and matrix multiply units (MXUs) to ...
Over at the NVIDIA blog, Loyd Case shares some recent advancements that deliver dramatic performance gains on GPUs to the AI community. We have achieved record-setting ResNet-50 performance for a ...
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