A mixed hardware-software approach to flexible Artificial Neural Network training on FPGA

@article{Aliaga2009AMH,
  title={A mixed hardware-software approach to flexible Artificial Neural Network training on FPGA},
  author={Ram{\'o}n Jos{\'e} Aliaga and Rafael Gadea Giron{\'e}s and Ricardo Jos{\'e} Colom-Palero and Joaqu{\'i}n Cerd{\'a} and N{\'e}stor Ferrando and Vicente Herrero},
  journal={2009 International Symposium on Systems, Architectures, Modeling, and Simulation},
  year={2009},
  pages={1-8}
}
FPGAs offer a promising platform for the implementation of Artificial Neural Networks (ANNs) and their training, combining the use of custom optimized hardware with low cost and fast development time. However, purely hardware realizations tend to focus on throughput, resorting to restrictions on applicable network topology or low-precision data representation, whereas flexible solutions allowing a wide variation of network parameters and training algorithms are usually restricted to software… CONTINUE READING

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