Architecture Specific Optimizations Sample Clauses

Architecture Specific Optimizations this methodology consists of assessing the acceleration opportunities for an implementation of the target algorithm that is optimized to run on the ARM host processor only, taking advantage of specific architectural features (e.g., SIMD engine).
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Architecture Specific Optimizations. The first set of experiments is aimed at assessing the acceleration opportunities for architecture-specific optimizations on the host processor. We consider three different implementations of the FAST benchmark (see FAST + BRIEF): • OpenCV: the original algorithm, contributed to OpenCV by the author, ▇▇▇▇▇▇ ▇▇▇▇▇▇. The algorithm has been substituted in OpenCV 2.3.1 with the optimized version (see below). • ▇▇▇▇▇▇: Machine-generated code, optimized for speed [Ros10]. It consists of an heuristic procedure derived from a machine learning tree. • FastCV: the FAST algorithm from the FastCV library. FastCV [QDNwᶟ] is a proprietary vision library distributed by Qualcomm providing a mobile-optimized computer vision library which includes the most frequently used vision processing functions for use across a wide array of ARM-based mobile devices. FastCV is designed for efficiency on all ARM-based processors, but is tuned to take advantage of Qualcomm's Snapdragon processor (S2 and above). In particular it uses vectorial instructions to perform multiple comparisons per cycle. OpenCV and ▇▇▇▇▇▇ algorithms are compiled with the g++ compiler included in the standard NDK toolchain, with instruction set ARMv7, NEON support disabled and optimization level -O2. The FastCV library is distributed in binary form and can execute in two distinct operative modes: Performance mode (1), which enables the SIMD unit, and Low Power mode (2), which only runs on the processor.
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