Fast Computing Framework for Convolutional Neural Networks
Marcin Korytkowski, Paweł Staszewski, Piotr Woldan, Rafał Scherer · 2016
In the case of building large convolutional neural networks, signal propagation speed is one of priority factors. Training large neural structures requires enormous time for achieving satisfying accuracy. In addition, the networks need to be learn by very large sets of good quality training images, which is another time-consuming factor. The paper presents a fast computing framework with some methods to optimize the signal propagation speed. We compare our implementation with the original OverFeat implementation.