Flexible IP cores for the k-NN classification problem and their FPGA implementation

Abstract

The k-nearest neighbor (k-NN) is a popular non-parametric benchmark classification algorithm to which new classifiers are usually compared. It is used in numerous applications, some of which may involve thousands of data vectors in a possibly very high dimensional feature space. For real-time classification a hardware implementation of the algorithm can deliver high performance gains by exploiting parallel processing and block pipelining. We present two different linear array architectures that have been described as soft parameterized IP cores in VHDL. The IP cores are used to synthesize and evaluate a variety of array architectures for a different k-NN problem instances and Xilinx FPGAs. It is shown that we can solve efficiently, using a medium size FPGA device, very large size classification problems, with thousands of reference data vectors or vector dimensions, while achieving very high throughput. To the best of our knowledge, this is the first effort to design flexible IP cores for the FPGA implementation of the widely used k-NN classifier.