A Scalable FPGA Implementation of Cellular Neural Networks for Gabor-type Filtering

• Main Authors: Cheung, O.Y.H., Leong, P.H.W., Tsang, E.K.C., Shi, B.E.
• Format: Conference Proceeding
• Language: English
• Subjects: Biological system modeling; Biology computing; Biomedical signal processing; Cellular neural networks; Energy consumption; Field programmable gate arrays; Gabor filters; Retina; Very large scale integration; Visual system
• ISSN: 2161-4393; 2161-4407
• DOI: 10.1109/IJCNN.2006.246653

We describe an implementation of Gabor-type filters on field programmable gate arrays using the cellular neural network (CNN) architecture. The CNN template depends upon the parameters (e.g., orientation, bandwidth) of the Gabor-type filter and can be modified at runtime so that the functionality of Gabor-type filter can be changed dynamically. Our implementation uses the Euler method to solve the ordinary differential equation describing the CNN. The design is scalable to allow for different pixel array sizes, as well as simultaneous computation of multiple filter outputs tuned to different orientations and bandwidths. For 1024 pixel frames, an implementation on a Xilinx Virtex XC2V1000-4 device uses 1842 slices, operates at 120 MHz and achieves 23,000 Euler iterations over one frame per second.