A Design Tool for High Performance Image Processing on Multicore Platforms
Jiahao Wu 1,a, Timothy Blattner 2,c, Walid Keyrouz 2,d and Shuvra S. Bhattacharyya 1,3,b
1University of Maryland, College Park, MD, USA
a jiahao@umd.edu
b ssb@umd.edu
2National Institute of Standards and Technology, Gaithersburg, MD, USA
c timothy.blattner@nist.gov
d walid.keyrouz@nist.gov
3Tampere University of Technology, Finland
ABSTRACT
Design and implementation of smart vision systems often involve the mapping of complex image processing algorithms into efficient, real-time implementations on multicore platforms. In this paper, we describe a novel design tool that is developed to address this important challenge. A key component of the tool is a new approach to hierarchical dataflow scheduling that integrates a global scheduler and multiple local schedulers. The local schedulers are lightweight modules that work independently. The global scheduler interacts with the local schedulers to optimize overall memory usage and execution time. The proposed design tool is demonstrated through a case study involving an image stitching application for large scale microscopy images.
