Implementing neural morphological operations using programmable logic

Neural network models have been studied for a number of years for achieving human-like performances in the fields of image and speech recognition. There has been a recent resurgence in the field of neural networks caused by new topologies and algorithms, analog VLSI implementation techniques, and the belief that massive parallelism is essential for high performance image and speech recognition. This paper presents an idea of implementing neural networks with Boolean programmable logic models. Though the approach didn't adopt continuous analog framework commonly used in related research, it can handle a variety of neural network applications and avoid some of the limitations of threshold logic networks. Dynamically programmable logic modules (or DPLM's) can be implemented with digital multiplexers. Each node performs a dynamically-assigned Boolean function of its input vectors. Therefore, the overall network is a combinational circuit and its outputs are Boolean global functions of the network's input variables.