GPU and FPGA parallelization of fuzzy cellular automata for the simulation of wildfire spreading

This paper presents a Fuzzy Cellular Automata (FCA) model with the aim to cope with the computational complexity and data uncertainties that characterize the simulation of wildfire spreading on real landscapes. Moreover, parallel implementations of the proposed FCA model, on both GPU and FPGA, are discussed and investigated. According to the results, the parallel models exhibit significant speedups over the corresponding sequential algorithm. As a possible application, the proposed model could be embedded on a portable electronic system for real-time prediction of fire spread scenarios.