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 analyzed 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.
['Chih-Hung Wu', 'Chin-Yuan Chiang', 'Yi-Han Chen']
Parallelism of Evolutionary Design of Image Filters for Evolvable Hardware Using GPU