Parallel resampling for particle filters on FPGAs

Abstract

Particle filters (PFs) are a set of algorithms that implement recursive Bayesian filtering, which represent the posterior distribution by a set of weighted samples. Resampling is a fundamental operation in PF algorithms. It consists of taking a population of samples and reconstructing it based on the weights attached to each sample, favouring the samples with large weights. However, resampling is computationally intensive when the number of samples is large and, most importantly, it is not inherently parallelizable like the other steps of the particle filter. Parallel computing devices such as Graphics Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs) have been proposed to accelerate resampling. In this paper, we propose novel parallel architectures that map four state-of-the-art resampling algorithms (systematic, residual systematic, Metropolis and Rejection resampling) to a FPGA. FPGA-specific optimisations are introduced to further optimize the performance of the above systems. The proposed architectures are implemented in a Virtex-6 LX240T FPGA device with half-utilization of logic resources. Compared to the respective state-of-the-art implementations on an NVIDIA K20 GPU, the achieved speedups are in the range of 1.7x-49x.