Power and performance characterization, analysis and tuning for energy-efficient edge detection on atom and ARM based platforms

The de facto standard for embedded platforms with medium to low computing demands are ARM with Thumb ISA and Intel Atom with the X86 ISA with multiple cores. Operating these architectures in the milliwatts range while running realtime computer vision corner detection algorithms is a challenging problem. We present the analysis of power, performance and energy-efficiency measurements of Harris corner detection across a wide range of voltage and frequency settings, multicore/multithreading strategies, and compiler and application optimization parameters to find how the interplay of these parameters affect the power, performance and energy-efficiency. Our measurement of results on state-of-the-art embedded platforms demonstrate that a systematic cross-layer optimization at the application level (Sobel filter type, aperture size, number of image tiles), compiler level (branch prediction, function inlining) and system level (voltage and frequency setting, single core vs multicore implementation) significantly improves the energy-efficiency of corner detection, while meeting its real-time performance constraints. This cross-layer optimization improves the energy-efficiency of Harris corner on Atom and ARM by 89.5% and 87.2%, respectively.