Novel dynamic partial reconfiguration implementation of k-means clustering on FPGAs: comparative results with GPPs and GPUs
Hanaa M. Hussain,Khaled Benkrid,Ali Ebrahim,Ahmet T. Erdogan,Huseyin Seker +4 more
- Vol. 2012, pp 1-15
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TLDR
A parameterized implementation of the K-means clustering algorithm in Field Programmable Gate Array (FPGA) is presented and compared with previous FPGA implementation as well as recent implementations on Graphics Processing Units (GPUs) and GPPs.Abstract:
K-means clustering has been widely used in processing large datasets in many fields of studies. Advancement in many data collection techniques has been generating enormous amounts of data, leaving scientists with the challenging task of processing them. Using General Purpose Processors (GPPs) to process large datasets may take a long time; therefore many acceleration methods have been proposed in the literature to speed up the processing of such large datasets. In this work, a parameterized implementation of the K-means clustering algorithm in Field Programmable Gate Array (FPGA) is presented and compared with previous FPGA implementation as well as recent implementations on Graphics Processing Units (GPUs) and GPPs. The proposed FPGA has higher performance in terms of speedup over previous GPP and GPU implementations (two orders and one order of magnitude, resp.). In addition, the FPGA implementation is more energy efficient than GPP and GPU (615x and 31x, resp.). Furthermore, three novel implementations of the K-means clustering based on dynamic partial reconfiguration (DPR) are presented offering high degree of flexibility to dynamically reconfigure the FPGA. The DPR implementations achieved speedups in reconfiguration time between 4x to 15x.read more
Citations
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Accelerating Medoids-based clustering with the Intel Many Integrated Core architecture
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Multiple-clone configuration of relocatable partial bitstreams in Xilinx Virtex FPGAs
TL;DR: The design and implementation of a novel internal reconfiguration engine which dynamically generates partial bitstreams required for simultaneous configuration of multiple clones of relocatable hardware tasks is demonstrated.
References
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Proceedings ArticleDOI
Algorithmic transformations in the implementation of K- means clustering on reconfigurable hardware
TL;DR: In mapping the k-means algorithm to FPGA hardware, this work examined algorithm level transforms that dramatically increased the achievable parallelism and also examined the effects of using fixed precision and truncated bit widths in the algorithm.
Proceedings Article
A parallel implementation of K-means clustering on GPUs
TL;DR: This paper introduces a first step towards building an efficient GPU-based parallel implementation of a commonly used clustering algorithm called K-Means on an NVIDIA G80 PCI express graphics board using the CUDA processing extensions.
Proceedings ArticleDOI
FPGA implementation of K-means algorithm for bioinformatics application: An accelerated approach to clustering Microarray data
TL;DR: This work proposes a highly parallel hardware design to accelerate the K-means clustering of Microarray data by implementing the K -means algorithm in Field Programmable Gate Arrays (FPGA).
Book ChapterDOI
Efficient K-Means Clustering Using Accelerated Graphics Processors
TL;DR: The novelties in the approach and techniques employed to represent data, compute distances, centroids and identify the cluster elements using the GPU are presented and performance is measured using the metric: computational time per iteration.
Proceedings ArticleDOI
Highly Parameterized K-means Clustering on FPGAs: Comparative Results with GPPs and GPUs
TL;DR: This work proposes a parameterized Field Programmable Gate Array (FPGA) implementation of the Kmeans algorithm and compares it with previous FPGA implementation as well as recent implementations on Graphics Processing Units (GPUs) and with GPPs.
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