A Computational Framework for Implementation of Neural Networks on Multi-Core Machine

This paper presents a computational framework, the Generic Programmable Neural Network (GPNN), for efficient implementation of Back-Propagation based neural learning algorithms running on multi-core machines. GPNN has three components: parallelization of neural learning, abstraction of network components, and compile-time generalization. Together these computational components make GPNN an efficient framework for fast implementation of back-propagation based neural learning algorithms, and provide flexibility and reusability for modifying neural network topologies. The GPNN was applied to four different neural learning algorithms: classic back-propagation (BP), quick propagation (QP), resilient propagation (RP) and Levenberg-Marquardt (LM) algorithm. Experiments were conducted to evaluate the effectiveness of GPNN, and results show that the neural learning algorithms implemented in GPNN are more efficient than their respective functions provided by Matlab.