Abstract

The growing performance and decreasing price of embedded processors are opening many doors, for both developers in the industry and in academia. However, the complexities of these systems can create serious developmental bottlenecks. Sophisticated software packages such as OpenCV can assist in both the functional development and educational aspects of these otherwise complex applications; such tools lend themselves very well to use by the academic community, in particular in providing examples of algorithm implementation. However the task of migrating this software to embedded platforms poses its own challenges. This paper will review how to mitigate some of these issues, including C++ implementation, memory constraints, floating-point support, and opportunities to maximise performance using vendor-optimised libraries and integrated accelerators or co-processors. Finally, we will introduce a new effort by Texas Instruments to optimise vision systems by running OpenCV on the C6000™ digital signal processor architecture. Benchmarks will show the advantage of using the DSP by comparing the performance of a DSP+ARM® system-on-chip (SoC) processor against an ARM-only device.

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