论文标题
与张量核心单元的相似性搜索
Similarity Search with Tensor Core Units
论文作者
论文摘要
张量核心单元（TCU）是为深神经网络开发的硬件加速器，有效地支持两个密集的$ \ sqrt {m} \ times \ times \ sqrt {m} $矩阵，其中$ m $是给定的硬件参数。在本文中，我们表明TCU也可以加快相似性搜索问题。我们为Johnson-Lindenstrauss尺寸降低的算法提出了算法，并通过利用TCUS实现$ \ sqrt {M} $加速，相对于传统方法，相似性。
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.
