abstract = "Evolvable Hardware (EHW) is a combination of
evolutionary algorithm and reconfigurable hardware
devices. Due to its flexible and adaptive ability,
EHW-based solutions receive a lot of attention in
industrial applications. One of the obstacles to
realize an EHW-based method is its very long training
time. This study deals with the parallelism of
EHW-based design of image filters using graphic
processing units (GPUs). The design process is analysed
and decomposed into some smaller processes that can run
in parallel. Pixel-based data for training and
verifying EHW solutions are partitioned according to
the architecture of GPU. Several strategies for
deploying parallel processes are developed and
implemented. With the proposed method, significant
improvements on the efficiency of training EHW models
are gained. Using a GPU with 240 cores, a speedup of 64
times is obtained. This paper evaluates and compares
the performance of the proposed method with other
ones.",
notes = "Dept. of Electr. Eng., Nat. Univ. of Kaohsiung,
Kaohsiung, Taiwan