Similarity Computation Using Reconfigurable Embedded Hardware

Abstract

Advances in portable devices and location-aware applications have necessitated the research in sophisticated yet small-footprint hardware and software in embedded systems, while the proliferation of the Web and distributed database systems has led to new data mining applications. We are investigating the utilization of reconfigurable hardware, due to its flexibility and performance, for data mining applications in portable and embedded computing. In this work, we introduce a reconfigurable hardware solution using Field Programmable Gate Array (FPGA) for similarity matrix computation, a commonly used data structure to represent the computed similarity among a set of feature vectors. Our hardware design can be dynamically reconfigured to accommodate three different similarity measures. A space-time cost analysis of the proposed multiplexer-based approach is presented. Experiments performed on the implemented reconfigurable hardware show encouraging and promising results that warrant further investigation in dynamically reconfigurable FPGA-based hardware for data mining applications.