Abstract
Many promising embedded computer vision applications, such as stereo estimation, rely on inference computation on Markov Random Fields (MRFs). Sequential Tree-Reweighted Message passing (TRW-S) is a superior MRF solving method, which provides better convergence and energy than others (e.g., belief propagation). Since software TRW-S solvers are slow, custom TRW-S hardware has been proposed to improve execution efficiency. This paper proposes hardware mechanisms to further optimize TRW-S hardware efficiency, by tracking differences in input message values (residues) and skipping computation when values no longer change (residue is zero). Evaluations of our hardware mechanisms using Middlebury benchmark show 1.6x to 6x potential reduction in computation (depending on design parameters) while increasing energy by only 0.4% to 4.8%.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
