Accelerating Large-Scale Convolutional Neural Networks with Parallel Graphics Multiprocessors

Training convolutional neural networks (CNNs) on large sets of high-resolution images is too computationally intense to be performed on commodity CPUs. Such architectures however achieve state-of-the-art results on low-resolution machine vision tasks such as the recognition of handwritten characters. We have adapted the inherent multi-level parallelism of CNNs for Nvidia's CUDA GPU architecture to accelerate the training by two orders of magnitude. This dramatic speedup permits to apply CNN architectures to pattern recognition tasks on datasets with high-resolution natural images.

• Type: Conference
• Year: 2010
• Where: ICANN
• Authors: Dominik Scherer, Hannes Schulz, Sven Behnke
• Keywords: Convolutional Neural Networks, ICANN 2010, Inherent Multi-level Parallelism, Neural Networks, Nvidia's CUDA GPU