Synthesis and Optimization of 2D Filter Designs for Heterogeneous FPGAs

Abstract

Many image processing applications require fast convolution of an image with one or more 2D filters. Field-Programmable Gate Arrays (FPGAs) are often used to achieve this goal due to their fine grain parallelism and reconfigurability. However, the heterogeneous nature of modern reconfigurable devices is not usually considered during design optimization. This article proposes an algorithm that explores the space of possible implementation architectures of 2D filters, targeting the minimization of the required area, by optimizing the usage of the different components in a heterogeneous device. This is achieved by exploring the heterogeneous nature of modern reconfigurable devices using a Singular Value Decomposition based algorithm, which provides an efficient mapping of filter's implementation requirements to the heterogeneous components of modern FPGAs. In the case of multiple 2D filters, the proposed algorithm also exploits any redundancy that exists within each filter and between different filters in the set, leading to designs with minimized area. Experiments with real filter sets from computer vision applications demonstrate an average of up to 38% reduction in the required area.