Model-based mapping of a nonrigid image registration algorithm to heterogeneous architectures

Abstract

This work targets the design of customized accelerators for image registration algorithms, which are required for many important computer vision applications. By capturing key, domain-specific characteristics of application structure, signal-processing-oriented models of computation provide a valuable foundation for structured development of efficient image registration accelerators. Building upon the meta-modeling framework of homogeneous parameterized dataflow, we develop in this paper an approach for automatically generating streamlined implementations of image registration algorithms according to performance metrics such as image size, area and overall processing speed. Results from hardware synthesis demonstrate the efficiency of our methods. Our approach provides designers an effective way to explore different architectures, and systematically provide acceleration for high-performance nonrigid image registration based on a variety of requirements. Our dataflow-based framework can be adapted to explore different architectures for other kinds of image processing algorithms as well.