Evaluation of an openCL-based FPGA platform for particle filter

Shunsuke Tatsumi, Masanori Hariyama, Norikazu Ikoma

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Particle filter is one promising method to estimate the internal states in dynamical systems, and can be used for various applications such as visual tracking and mobile-robot localization. The major drawback of particle filter is its large computational amount, which causes long computational-time and large power- consumption. In order to solve this problem, this paper proposes an Field-Programmable Gate Array (FPGA) platform for particle filter. The platform is designed using the OpenCL-based design tool that allows users to develop using a high-level programming language based on C and to change designs easily for various applications. The implementation results demonstrate the proposed FPGA implementation is 106 times faster than the CPU one, and the power-delay product of the FPGA implementation is 1.1% of the CPU one. Moreover, implementations for three different systems are shown to demonstrate flexibility of the proposed platform.

Original languageEnglish
Pages (from-to)743-754
Number of pages12
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Issue number5
Publication statusPublished - 2016 Sept


  • Monte carlo method
  • OpenCL. FPGA
  • Parallel processing
  • Particle filter


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