Area Efficient Box Filter Acceleration by Parallelizing with Optimized Adder Tree

Abstract

Box filters are widely used in image and video processing applications. To achieve the real-time performance for these applications, designers may need to parallelize these box filters. However, it is very challenging to implement a parallel box filter on modern programmable system-on-chip (SoC). On one hand, the dependency between the operations of a box filter is too strong to achieve parallelism. On the other hand, more adder trees are required as the degree of parallelism increases. In this paper, we propose a performance and area efficient boxfilter. It uses the partial sum difference, which needs much less resources, to effectively calculate the box filter. We make the full use of this reusable partial sum to optimize the adder trees for parallel processing. We also make two case studies of the box filter by applying it to the guided filter and the stereo matching algorithm on a programmable SoC using a C-based design flow. Our method removes the dependencies between the parallel operations of the box filter. Compare to the state-of-the-art, results show that the computational complexity of the adder tree for a single pixel has been reduced from O(R^2) to O((R+N)lgN/N ) on average. There are orders of magnitude reduction in resource usage with large filter size R and parallelization degree N. The throughput can be increased by N times, where N is up to 72 in the case of Xilinx FPGA board XCZU9EG.