@inproceedings{92f84828efbb4359ac4427f3bd5f0b45,
title = "A Hadoop-based Principle Component Analysis on embedded heterogeneous platform",
abstract = "Hadoop is a widely adopted distributed processing framework which assumes each computing node a CPU-based system with local memory. This design scheme cannot effectively take full advantage of an embedded heterogeneous many-core platform due the mismatch of data collection and management paradigms between the Hadoop environment and embedded heterogeneous systems. This paper proposes a Hadoop-based design of Principle Component Analysis (PCA) to efficiently leverage the distributed embedded heterogeneous many-core systems. By taking the same data layout of conventional Hadoop applications, the proposed design introduces efficient manners to collect and manage the fine-grained data chunks. The experiments on a Tegra K1 has achieved 5.9× performance enhancement.",
author = "Chen, {Sheng Yen} and Wei, {Chia I.} and Chiu, {Yu Chen} and Bo-Cheng Lai",
year = "2017",
month = jun,
day = "5",
doi = "10.1109/VLSI-DAT.2017.7939667",
language = "English",
series = "2017 International Symposium on VLSI Design, Automation and Test, VLSI-DAT 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2017 International Symposium on VLSI Design, Automation and Test, VLSI-DAT 2017",
address = "美國",
note = "2017 International Symposium on VLSI Design, Automation and Test, VLSI-DAT 2017 ; Conference date: 24-04-2017 Through 27-04-2017",
}