Neural network based edge detection with pulse mode operations and floating point format precision

This paper proposes a pulse mode neural network based edge detection system. Edge detection of an image reduces significantly the amount of data and filters out information that may be regarded as less irrelevant. Edge detection is efficient in medical imaging. Known by their significant compactness pulse mode neural networks are becoming an attractive solution for function approximation based on frequency modulation. Early pulse mode implementation suffers from some network constraints due to weight range limitations. To provide the best edge detection, the backpropagation algorithm is modified to have pulse mode operations for effective hardware implementation. Here, we undergo these limitations with a new pulse mode network architecture using floating point operations in the activation function. By using floating point number system for synapse weight value representation, any function can be approximated by the network. The proposed pulse mode MNN is used to detect the edges in images forming a heterogenous data base. It shows good learning capability. The corresponding design was implemented into a virtex II PRO XC2VP7 Xilinx FPGA.