Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
410807 | Neurocomputing | 2007 | 7 Pages |
Abstract
Several implementations of Feedforward Neural Networks have been reported in scientific papers. These implementations do not allow the direct use of off-line trained networks. Usually, the problem is the lower precision (compared to the software used for training) or modifications in the activation function. In the present work, a hardware solution called Artificial Neural Network Processor, using a FPGA, fits the requirements for a direct implementation of Feedforward Neural Networks, because of the high precision and accurate activation function that were obtained. The resulting hardware solution is tested with data from a real system to confirm that it can correctly implement the models prepared off-line with MATLAB.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Pedro Ferreira, Pedro Ribeiro, Ana Antunes, Fernando Morgado Dias,