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Tensor Core Units (TCUs) are hardware accelerators developed for deep neural networks, which efficiently support the multiplication of two dense √(m)×√(m) matrices, where m is a given hardware parameter. In this paper, we show that TCUs can speed up similarity search problems as well. We propose algorithms for the Johnson-Lindenstrauss dimensionality reduction and for similarity join that, by leveraging TCUs, achieve a √(m) speedup up with respect to traditional approaches.arXiv:2006.12608v1 fatcat:qji5hmb7wvgnjdiy2l3autqh4e