FIGUEIREDO, L. O.; FIGUEIREDO, Lucas Oliveira de.
Resumo:
The proliferation of the use of neural networks for classification issues, the availability of
tools to describe and train such networks in cloud infrastrucutres and numerical computing
softwares, results in a reduction of the work load related to the deployment of neural network
solutions. Limitations in performance of processors and graphical units, in comparison
to what FPGAs have to offer in its support to parallel computing and under demand
design, has make these platforms as first choice in hardware acceleration for trained neural
networks models. The several proficiencies required to fulfill a development flow comprising
the training of a network and its description in hardware have their details studied in this
undergraduate thesis.