Publications from our Researchers

Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.  


BibTex format

author = {Ma, ZB and Yang, Y and Liu, YX and Bharath, AA},
doi = {10.1109/TCSII.2016.2536202},
journal = {IEEE Transactions on Circuits and Systems},
pages = {979--983},
title = {Recurrently decomposable 2-D convolvers for FPGA-based digital image processing},
url = {},
volume = {63},
year = {2016}

RIS format (EndNote, RefMan)

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 -
UR -
VL - 63
ER -