脉冲耦合神经网络的并行加速优化算法研究

The Study of Parallel Programming Optimization of Pulse Coupled Neural Network

并行编程技术可以有效提高算法的执行效率。文中分别利用CPU的单指令多数据流扩展指令集(Streaming SIMD Extensions,SSE)技术和多核并行编程技术,对脉冲耦合神经网络(Pulse Coupled Neural Network,PCNN)分割算法进行并行编程优化,以减少算法的运行时间。实验结果表明,SSE技术以及多核并行编程技术大大加快了PCNN分割算法的运行速度,有效提高了算法的执行效率,在一定程度上解决了该方法计算量大、耗时多的问题,具有应用于医学图像处理的潜在价值。

The parallel programming technique can improve the algorithm＇s running efficiency.In this study,the Streaming SIMD Extensions（SSE）instructions in CPU and multi-core parallel programming technique were respectively used to optimize the pulse coupled neural network（PCNN）based segmentation algorithm so as to reduce the execution time.The experimental results indicated that SSE and multi-core parallel programming technique could speed up the PCNN-based segmentation algorithm,improve the running efficiency,and solve the problems of large computation and long time consume for PCNN at a certain extent.The results suggest that parallel programming has the potential to be used in medical image processing.