Using Wavelet Transform and Partial Distance Search to Implement kNN Classifier on FPGA with Multiple Modules

Abstract

This paper presents a novel algorithm of using wavelet transform and partial distance search (PDS) to realize the kNN classifier on field programmable gate array (FPGA) with multiple modules. The algorithm identifies first k closest vectors in the design set of a kNN classifier for each input vector by performing the PDS in the wavelet domain, and allows concurrent classification of different input vectors for further computation acceleration by employing multiple-module PDS. For the effective reduction of the area complexity and computation latency, we proposed a novel PDS algorithm well-suited for hardware implementation and also employ subspace search, bitplane reduction and multiple-coefficient accumulation techniques. The proposed realization has been embedded in a softcore CPU for physical performance measurements. Experimental results show that the proposed realization not only provides a cost-effective solution to the FPGA implementation of kNN classification systems, but also meets both high throughput and low area cost.