Tensor Core Units (TCUs) are hardware accelerators developed for deep neural networks, which efficiently support the multiplication of two dense $\sqrt{m}\times \sqrt{m}$ 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 $\sqrt{m}$ speedup up with respect to traditional approaches.

中文翻译：

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使用张量核心单元进行相似性搜索

张量核心单元 (TCU) 是为深度神经网络开发的硬件加速器，它有效地支持两个密集的 $\sqrt{m}\times \sqrt{m}$ 矩阵的乘法，其中 $m$ 是给定的硬件参数。在本文中，我们展示了 TCU 也可以加速相似性搜索问题。我们提出了 Johnson-Lindenstrauss 降维和相似性连接的算法，通过利用 TCU，实现了相对于传统方法的 $\sqrt{m}$ 加速。