An Efficient FPGA Implementation of a DT-CNN for Small Image Gray-scale Pre-processing

This paper presents an 8-bit FPGA implementation of a Discrete Time Cellular Neural Network (DTCNN) suitable for small image gray-scale pre-processing (simple operations with high computational burden). It uses Split&Shift techniques to have a 31 x 31 grid that processes more than 2500 images per second. As this work evolves from a previous binary DTCNN implementation, results are compared in terms of area occupancy, routing complexity and processing time. Several design techniques have been applied to optimize the VHDL implementation on an Altera Stratix II-EP2S60F484C5 FPGA device. Moreover, as technology independent description allows easy migration to other devices or vendors, the benefits of FPGA technology evolution are discussed, focusing on DTCNN implementations.