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- W4316021483 abstract "The K-Nearest Neighbor algorithm is a supervised machine learning algorithm that is used for classification problems. The execution time of this algorithm could be extremely high, especially for huge and high-dimensional datasets. The objective of this work is to design and implement efficient parallel hardware architectures to accelerate the KNN classifier. The proposed architectures are implemented using FPGA on Intel DevCloud. Experimental results show that the proposed hardware implementation of the algorithm is 10.7 times faster than the software implementation with 96.6% classification accuracy for a benchmark classification dataset." @default.
- W4316021483 created "2023-01-14" @default.
- W4316021483 creator A5041107857 @default.
- W4316021483 creator A5049637855 @default.
- W4316021483 date "2022-12-04" @default.
- W4316021483 modified "2023-10-18" @default.
- W4316021483 title "Cloud-Based Reconfigurable Hardware Accelerator for the KNN Classification Algorithm" @default.
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- W4316021483 doi "https://doi.org/10.1109/cicn56167.2022.10008343" @default.
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