FPGA implementation comparison between C-Mantec and Back-Propagation neural network algorithms

Recent advances in FPGA technology have permitted the implementation of neurocomputational models, making them an interesting alternative to standard PCs in order to speed up the computations involved taking advantage of the intrinsic FPGA parallelism. In this work, we analyse and compare the FPGA implementation of two neural network learning algorithms: the standard Back-Propagation algorithm and C-Mantec, a constructive neural network algorithm that generates compact one hidden layer architectures. One of the main differences between both algorithms is the fact that while Back-Propagation needs a predefined architecture, C-Mantec constructs its network while learning the input patterns. Several aspects of the FPGA implementation of both algorithms are analysed, focusing in features like logic and memory resources needed, transfer function implementation, computation time, etc. Advantages and disadvantages of both methods are discussed in the context of their application to benchmark problems.