This article presents an approach for mapping real-time applications based on particle filters (PFs) to
heterogeneous reconfigurable systems, which typically consist of multiple FPGAs and CPUs. A method is
proposed to adapt the number of particles dynamically and to utilise runtime reconfigurability of FPGAs for
reduced power and energy consumption. A data compression scheme is employed to reduce communication
overhead between FPGAs and CPUs. A mobile robot localisation and tracking application is developed to
illustrate our approach. Experimental results show that the proposed adaptive PF can reduce up to 99% of
computation time. Using runtime reconfiguration, we achieve a 25% to 34% reduction in idle power. A 1U
system with four FPGAs is up to 169 times faster than a single-core CPU and 41 times faster than a 1U
CPU server with 12 cores. It is also estimated to be 3 times faster than a system with four GPUs.