NDLI: Low Power and Scalable Many-Core Architecture for Big-Data Stream Computing

Content Provider	IEEE Xplore Digital Library
Author	Kanoun, K. Ruggiero, M. Atienza, D. van der Schaar, M.
Copyright Year	2014
Description	Author affiliation: Ecole Polytech. Fed. de Lausanne (EPFL), Lausanne, Switzerland (Kanoun, K.; Ruggiero, M.; Atienza, D.) \|\| Univ. of California, Los Angeles, Los Angeles, CA, USA (van der Schaar, M.)
Abstract	In the last years the process of examining large amounts of different types of data, or Big-Data, in an effort to uncover hidden patterns or unknown correlations has become a major need in our society. In this context, stream mining applications are now widely used in several domains such as financial analysis, video annotation, surveillance, medical services, traffic prediction, etc. In order to cope with the Big-Data stream input and its high variability, modern stream mining applications implement systems with heterogeneous classifiers and adapt online to its input data stream characteristics variation. Moreover, unlike existing architectures for video processing and compression applications, where the processing units are reconfigurable in terms of parameters and possibly even functions as the input data is changing, in Big-Data stream mining applications the complete computing pipeline is changing, as entirely new classifiers and processing functions are invoked depending on the input stream. As a result, new approaches of reconfigurable hardware platform architectures are needed to handle Big-Data streams. However, hardware solutions that have been proposed so far for stream mining applications either target high performance computing without any power consideration (i.e., limiting their applicability in small-scale computing infrastructures or current embedded systems), or they are simply dedicated to a specific learning algorithm (i.e., limited to run with a single type of classifiers). Therefore, in this paper we propose a novel low-power many-core architecture for stream mining applications that is able to cope with the dynamic data-driven nature of stream mining applications while consuming limited power. Our exploration indicates that this new proposed architecture is able to adapt to different classifiers complexities thanks to its multiple scalable vector processing units and their re-configurability feature at run-time. Moreover, our platform architecture includes a memory hierarchy optimized for Big-Data streaming and implements modern fine-grained power management techniques over all the different types of cores allowing then minimum energy consumption for each type of executed classifier.
Sponsorship	IEEE Comput. Soc.
Starting Page	468
Ending Page	473
File Size	586335
Page Count	6
File Format	PDF
e-ISBN	9781479937653
DOI	10.1109/ISVLSI.2014.77
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Memory management Data mining Streaming media Graphics processing units Buffer storage Hardware Reconfigurable Big-Data Low-Power Many-core Memory Hierarchy Streaming application Stream Mining Application Online
Content Type	Text
Resource Type	Article

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