Neural, Parallel & Scientific Computations
Using GPUs for Machine Learning Algorithms
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Fast support vector machine training and classification on graphics processors
Proceedings of the 25th international conference on Machine learning
A performance study of general-purpose applications on graphics processors using CUDA
Journal of Parallel and Distributed Computing
Neural Network Implementation Using CUDA and OpenMP
DICTA '08 Proceedings of the 2008 Digital Image Computing: Techniques and Applications
Radial Basis Function Networks GPU-Based Implementation
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
Graphics Processing Units (GPUs) can provide remarkable performance gains when compared to CPUs for computationally-intensive applications. In the biomedical area, most of the previous studies are focused on using Neural Networks (NNs) for pattern recognition of biomedical signals. However, the long training times prevent them to be used in real-time. This is critical for the fast detection of Ventricular Arrhythmias (VAs) which may cause cardiac arrest and sudden death. In this paper, we present a parallel implementation of the Back-Propagation (BP) and the Multiple Back-Propagation (MBP) algorithm which allowed significant training speedups. In our proposal, we explicitly specify data parallel computations by defining special functions (kernels ); therefore, we can use a fast evaluation strategy for reducing the computational cost without wasting memory resources. The performance of the pattern classification implementation is compared against other reported algorithms.