Аннотация
Due to their flexible architecture and inherent parallelism, FPGAs are ideal candidates for neural network implementations. Still they have not achieved wide-spread acceptance in this regard. One of the major roadblocks for FPGAs is the implementation of complex mathematical functions encountered in neural networks. Exact implementation of these functions consume large number of resources. In this paper we discuss an FPGA-based neural network prototyping platform and the approximate implementation of a probabilistic neural network (PNN) on a Xilinx 7-Series FPGA. The complex mathematical functions as replaced by approximations. Analysis shows that hardware performance is much higher than that of software counter parts and the error induced due to approximations is within tolerable limit.