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|Title:||Recurrently decomposable 2-D convolvers for FPGA-based digital image processing|
|Keywords:||Science & Technology;Technology;Engineering, Electrical & Electronic;Engineering;Field-programmable gate arrays (FPGAs);large convolution mask;recurrently decomposable (RD);two-dimensional (2-D) convolution;REAL-TIME COMPUTATION;ARCHITECTURE;CONVOLUTION;Electrical & Electronic Engineering; Electrical And Electronic Engineering|
|Description:||Two-dimensional (2-D) convolution is a widely used operation in image processing and computer vision, characterized by intensive computation and frequent memory accesses. Previous efforts to improve the performance of field-programmable gate array (FPGA) convolvers focused on the design of buffering schemes and on minimizing the use of multipliers. A recently proposed recurrently decomposable (RD) filter design method can reduce the computational complexity of 2-D convolutions by splitting the convolution between an image and a large mask into a sequence of convolutions using several smaller masks. This brief explores how to efficiently implement RD based 2-D convolvers using FPGA. Three FPGA architectures are proposed based on RD filters, each with a different buffering scheme. The conclusion is that RD based architectures achieve higher area efficiency than other previously reported state-of-the-art methods, especially for larger convolution masks. An area efficiency metric is also suggested, which allows the most appropriate architecture to be selected.|
|Type Of Material:||Other|
|Appears in Collections:||Bioengineering|
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