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Erschienen in:
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2019 | OriginalPaper | Buchkapitel

Data-Intensive Computing Acceleration with Python in Xilinx FPGA

verfasst von : Yalin Yang, Linjie Xu, Zichen Xu, Yuhao Wang

Erschienen in: Data Quality and Trust in Big Data

Verlag: Springer International Publishing

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Abstract

Data-intensive workloads drive the development of hardware design. Such data intensive services are driven the raising trend of novel machine learning techniques, such as CNN/RNN, over massive chunks of data objects. These services require novel devices with configurable high throughput in I/O (i.e., data-based model training), and uniquely large computation capability (i.e., large number of convolutional operations). In this paper, we present our early work on realizing a python-based Field-Programmable Gate Array (FPGA) system to support such data-intensive services. In our current system, we deploy a light layer of CNN optimization and a mixed hardware setup, including multiple FPGA/GPU nodes, to provide performance acceleration on the run. Our prototype can support popular machine learning platform, such as Caffe, etc. Our initial empirical results show that our system can perfect handling all data-intensive learning services.

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Vorheriges Kapitel A Formal Taxonomy of Temporal Data Defects
Nächstes Kapitel Delone and McLean IS Success Model for Evaluating Knowledge Sharing
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Metadaten
Titel
Data-Intensive Computing Acceleration with Python in Xilinx FPGA
verfasst von
Yalin Yang
Linjie Xu
Zichen Xu
Yuhao Wang
Copyright-Jahr
2019
Buch
Data Quality and Trust in Big Data

Print ISBN: 978-3-030-19142-9

Electronic ISBN: 978-3-030-19143-6

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-19143-6_8