Improvements in multiprocessor system design
ISCA '85 Proceedings of the 12th annual international symposium on Computer architecture
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
Experience with a Hybrid Processor: K-Means Clustering
The Journal of Supercomputing
Face Recognition Using A DCT-HMM Approach
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
Proceedings of the 2004 Asia and South Pacific Design Automation Conference
Real-time K-Means Clustering for Color Images on Reconfigurable Hardware
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Accelerating K-Means on the Graphics Processor via CUDA
INTENSIVE '09 Proceedings of the 2009 First International Conference on Intensive Applications and Services
K-Means on Commodity GPUs with CUDA
CSIE '09 Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering - Volume 03
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
Least squares quantization in PCM
IEEE Transactions on Information Theory
Implementation issues of neuro-fuzzy hardware: going toward HW/SW codesign
IEEE Transactions on Neural Networks
A digital architecture for support vector machines: theory, algorithm, and FPGA implementation
IEEE Transactions on Neural Networks
Kerneltron: support vector "machine" in silicon
IEEE Transactions on Neural Networks
High-speed face recognition based on discrete cosine transform and RBF neural networks
IEEE Transactions on Neural Networks
Flexible Hardware Architecture of Hierarchical K-Means Clustering for Large Cluster Number
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
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In this paper, we report a hardware/software (HW/SW) co-designed K-means clustering algorithm with high flexibility and high performance for machine learning, pattern recognition and multimedia applications. The contributions of this work can be attributed to two aspects. The first is the hardware architecture for nearest neighbor searching, which is used to overcome the main computational cost of a K-means clustering algorithm. The second aspect is the high flexibility for different applications which comes from not only the software but also the hardware. High flexibility with respect to the number of training data samples, the dimensionality of each sample vector, the number of clusters, and the target application, is one of the major shortcomings of dedicated hardware implementations for the K-means algorithm. In particular, the HW/SW K-means algorithm is extendable to embedded systems and mobile devices. We benchmark our multi-purpose K-means system against the application of handwritten digit recognition, face recognition and image segmentation to demonstrate its excellent performance, high flexibility, fast clustering speed, short recognition time, good recognition rate and versatile functionality.