FPGA implementation of artificial neural networks: an application on medical expert systems

In this paper, the FPGA implementation of an Artificial Neural Networks (ANNs) composition for a Medical Expert System (MES) focused on pulmonary diseases is discussed. Using a specially designed neuron based on pipelined bit-serial arithmetic and a successful approximation of its determinant sigmoid function, a computation module has been structured that can accommodate eight (8) neurons in one FPGA. The use of memory elements allows for up to 256 K synapses to be mapped with high speed and great accuracy performances. Also, due to the FPGA reconfigurability, new structures and training patterns can be used to update this MES, in order to fit in more pulmonary or other diseases, with minimal effort.