{"title":"High level prototyping and FPGA implementation of the orthogonal matching pursuit algorithm","authors":"P. Blache, H. Rabah, A. Amira","doi":"10.1109/ISSPA.2012.6310501","DOIUrl":null,"url":null,"abstract":"In this paper we present a novel hardware architecture for reconstruction of signals in compressed sensing. The proposed architecture is based on the orthogonal matching pursuit (OMP) algorithm which has been modeled with Simulink and implemented on FPGA using Xilinx system generator. The main aim is to optimize both area and execution time. The execution time is reduced by exploiting parallelism inside each kernel, where the area is reduced by reusing several operators such as matrix vector multiplication. Hardware implementation on the Virtex5 FPGA has shown excellent results compared to existing implementations. Moreover, our solution achieves a speedup of 38 compared to a software solution on the Intel core duo CPU.","PeriodicalId":248763,"journal":{"name":"2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA)","volume":"331 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"39","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSPA.2012.6310501","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 39
Abstract
In this paper we present a novel hardware architecture for reconstruction of signals in compressed sensing. The proposed architecture is based on the orthogonal matching pursuit (OMP) algorithm which has been modeled with Simulink and implemented on FPGA using Xilinx system generator. The main aim is to optimize both area and execution time. The execution time is reduced by exploiting parallelism inside each kernel, where the area is reduced by reusing several operators such as matrix vector multiplication. Hardware implementation on the Virtex5 FPGA has shown excellent results compared to existing implementations. Moreover, our solution achieves a speedup of 38 compared to a software solution on the Intel core duo CPU.