Measurement-Based Power Optimization Technique for OpenCV on Heterogeneous Multicore Processor

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Abstract
Today’s embedded systems often operate computer-vision applications, and are associated with timing and power constraints. Since it is not simple to capture the symmetry between the application and the model, the model-based design approach is generally not applicable to the optimization of computer-vision applications. Thus, in this paper, we propose a measurement-based optimization technique for an open-source computer-vision application library, OpenCV, on top of a heterogeneous multicore processor. The proposed technique consists of two sub-systems: the optimization engine running on a separate host PC, and the measurement library running on the target board. The effectiveness of the proposed optimization technique has been verified in the case study of latency-power co-optimization by using two OpenCV applications—canny edge detection and squeezeNet. It has been shown that the proposed technique not only enables broader design space exploration, but also improves optimality.
Funding Information
  • Ministry of Science and ICT, South Korea (IITP-2019-2018-0-01424)
  • National Research Foundation of Korea (2019R1F1A1064209)

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