A Machine-Learning Strategy for Protein Analysis
IEEE Intelligent Systems
Knowledge Discovery in Multi-label Phenotype Data
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Robust Real-Time Face Detection
International Journal of Computer Vision
Multi-class pattern classification using neural networks
Pattern Recognition
Backpropagation applied to handwritten zip code recognition
Neural Computation
Parallel implementation of Artificial Neural Network training for speech recognition
Pattern Recognition Letters
A multi-GPU algorithm for large-scale neuronal networks
Concurrency and Computation: Practice & Experience
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Multi-class pattern classification has a variety of applications and could be achieved using artificial neural networks (ANN). There are two major system architectures for using ANNs in multi-class pattern classification: using a single ANN and using multiple ANNs. Independent of what architecture is used, one of the main concerns of using ANNs is that with increasing number of pattern classes and training datasets, the training time will increase dramatically which renders the ANN unfeasible. In this paper, the vast computational power of Graphics Processing Units (GPUs) is utilized to mitigate this problem. Different architectures and different methods of feeding pattern classes are implemented in a GPU platform. Different methods have been proposed to achieve maximum parallelism and subsequently maximize throughput. Our implementation exceeds the state-of-the-art in literature in terms of speed and the accurate use of GPU resources. As a result, the proposed approach's run time is about 75% shorter than the previous approaches. In multi-ANN architecture, due to the inherent parallelism in the proposed implementation, the execution time of a system for a digit recognition application is reduced from seven hours in CPU to about 4 seconds in GPU.