High speed c-means clustering in reconfigurable hardware
Microprocessors & Microsystems
VFloat: A Variable Precision Fixed- and Floating-Point Library for Reconfigurable Hardware
ACM Transactions on Reconfigurable Technology and Systems (TRETS)
Bandwidth adaptive hardware architecture of K-Means clustering for video analysis
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Active storage networks for accelerating K-means data clustering
ARC'11 Proceedings of the 7th international conference on Reconfigurable computing: architectures, tools and applications
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Unsupervised clustering is a powerful technique for understanding multispectral and hyperspectral images, being k-means one of the most used iterative approaches. It is a simple though computationally expensive algorithm, particularly for clustering large hyperspectral images into many categories. Software implementation presents advantages such as flexibility and low cost for implementation of complex functions. However, it presents limitations, such as difficulties to exploit parallelism for high performance applications. In order to accelerate the k-means clustering a hardware implementation could be used. The disadvantage in this approach is that any change in the project requires previous knowledge of the hardware design process and can take several weeks to be implemented. In order to improve the design methodology, an automatic and parameterized implementation for hyperspectral imageshas been developed in a hardware/software codesign approach. An unsupervised clustering technique k-means that uses the Euclidian Distance to calculate the pixel to centers distance was used as a case study to validate the methodology. Two implementations, a software and a hardware/software codesign ones, have been implemented. Although the hardware component operates in 40MHz, being 12.5 times lesser than the software operating frequency (PC), the codesign implementation was approximately 2 times faster than software one.