Content-based image retrieval algorithm acceleration in a low-cost reconfigurable FPGA cluster

Authors:
C. Pedraza;E. Castillo;J. Castillo;J. L. Bosque;J. I. Martinez;O. D. Robles;J. Cano;P. Huerta
Affiliations:
Universidad Rey Juan Carlos, DATCCCIA, Escuela Técnica Superior de Ingeniería Informática, Móstoles, Madrid 28933, Spain;Universidad de Cantabria, Facultad de Ciencias, Dpto. de Electrónica y Computadores, Santander, Spain;Universidad Rey Juan Carlos, DATCCCIA, Escuela Técnica Superior de Ingeniería Informática, Móstoles, Madrid 28933, Spain;Universidad de Cantabria, Facultad de Ciencias, Dpto. de Electrónica y Computadores, Santander, Spain;Universidad Rey Juan Carlos, DATCCCIA, Escuela Técnica Superior de Ingeniería Informática, Móstoles, Madrid 28933, Spain;Universidad Rey Juan Carlos, DATCCCIA, Escuela Técnica Superior de Ingeniería Informática, Móstoles, Madrid 28933, Spain;Universidad Rey Juan Carlos, DATCCCIA, Escuela Técnica Superior de Ingeniería Informática, Móstoles, Madrid 28933, Spain;Universidad Rey Juan Carlos, DATCCCIA, Escuela Técnica Superior de Ingeniería Informática, Móstoles, Madrid 28933, Spain
Venue:
Journal of Systems Architecture: the EUROMICRO Journal
Year:
2010

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Abstract

The SMILE project main aim is to build an efficient low-cost cluster based on FPGA boards in order to take advantage of its reconfigurable capabilities. This paper shows the cluster architecture, describing: the SMILE nodes, the high-speed communication network for the nodes and the software environment. Simulating complex applications can be very hard, therefore a SystemC model of the whole system has been designed to simplify this task and provide error-free downloading and execution of the applications in the cluster. The hardware-software co-design process involved in the architecture and SystemC design is presented as well. The SMILE cluster functionality is tested executing a real complex Content-Based Information Retrieval (CBIR) parallel application and the performance of the cluster is compared (time, power and cost) with a traditional cluster approach.