Tensor Core Units (TCUs) are hardware accelerators developed for deep neural networks, which efficiently support the multiplication of two dense $$sqrt{m} imes sqrt{m}$$ 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 $$arOmega (sqrt{m})$$ speedup up with respect to traditional approaches.
Similarity search with tensor core units
Silvestri F.
2020
Abstract
Tensor Core Units (TCUs) are hardware accelerators developed for deep neural networks, which efficiently support the multiplication of two dense $$sqrt{m} imes sqrt{m}$$ 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 $$arOmega (sqrt{m})$$ speedup up with respect to traditional approaches.File in questo prodotto:
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