TY - JOUR AB - 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. AU - Ma,ZB AU - Yang,Y AU - Liu,YX AU - Bharath,AA DO - 10.1109/TCSII.2016.2536202 EP - 983 PY - 2016/// SN - 1549-7747 SP - 979 TI - Recurrently decomposable 2-D convolvers for FPGA-based digital image processing T2 - IEEE Transactions on Circuits and Systems UR - http://dx.doi.org/10.1109/TCSII.2016.2536202 UR - http://hdl.handle.net/10044/1/33197 VL - 63 ER -