多维视觉数据聚类张量逼近的硬件结构与实现

C. Yang, Yang-Ming Yeh, Yi-Chang Lu
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引用次数: 0

摘要

张量近似已被证明是一种高效、灵活的降维方法。然而,对于需要快速绘制图像的应用,应用张量近似后的数据重建计算成本仍然很高。因此,提出了几种支持快速重建的改进张量近似算法,其中聚类张量近似(CTA)是其中经常被提及的算法之一。本文设计了一种CTA硬件加速器。处理器可以处理大小为$12\ mathm {S}\乘以12\ mathm {S}\乘以12\ mathm {S}\乘以12\ mathm {S}$的张量。采用并行处理技术,与软件相比,处理器的性能可以实现9.41倍的加速。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hardware Architecture and Implementation of Clustered Tensor Approximation for Multi-Dimensional Visual Data
Tensor approximation has been proven to be an efficient and flexible dimensionality reduction method. However, for applications which require rapid image rendering, the computational cost of data reconstruction after applying tensor approximation is still high. As a result, several modified tensor approximation algorithms supporting fast reconstruction have been proposed, where clustered tensor approximation (CTA) is one of those which are often mentioned. In this paper, we design a hardware accelerator for CTA. The processor can handle a tensor of size $12\mathrm{S}\times 12\mathrm{S}\times 12\mathrm{S}\times 12\mathrm{S}$. With parallel processing techniques, the performance of the processor can achieve a $ 9.41\times $ speed-up when compared to the software.
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