Implementing neural network in custom computers

This paper describes the implementation of a partially connected neural network using FPGAs (field programmable gate arrays) based custom computers. Starting from the training data, a decision tree is generated using the classifier program C4.5. The tree is then used to initialise the architecture of the neural network to a nearly optimum configuration. This initialised partially connected network is then trained using training data. The trained neural network is then implemented by fine-grain Xilinx XC6200 series FPGAs. This implementation requires fewer connections and can provide a very high speed classification for many real-time image recognition applications