Evolutionary neural network (ENN) shows high performance in function optimization and in finding approximately global optima from searching large and complex spaces. It is one of the most efficient and adaptive optimization techniques used widely to provide candidate solutions that lead to the fitness of the problem. ENN has the extraordinary ability to search the global and learning the approximate optimal solution regardless of the gradient information of the error functions. However, ENN requires high computation and processing which requires parallel processing platforms such as field programmable gate arrays (FPGAs) and graphic processing units (GPUs) to achieve a good performance. This work involves different new implementations of ENN by exploring and adopting different techniques and opportunities for parallel processing. Different versions of ENN algorithm have also been implemented and parallelized on FPGAs platform for low latency by exploiting the parallelism and pipelining approaches. Real data form mass spectrometry data (MSD) application was tested to examine and verify our implementations. This is a very important and extensive computation application which needs to search and find the optimal features (peaks) in MSD in order to distinguish cancer patients from control patients. ENN algorithm is also implemented and parallelized on single core and GPU platforms for comparison purposes. The computation time of our optimized algorithm on FPGA and GPU has been improved by a factor of 6.75 and 6, respectively.
Parallel processing optimization strategy based on MapReduce model in cloud storage environment
