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Conference Papers Year : 2018

Algorithm-level Approximation for Fast (or not) Embedded Stereovision Algorithm


Because of the growing concern towards the energy consumption of embedded devices, the quality of an application is now considered as a new tunable parameter during the implementation phase. Approximations are then deliberately introduced to gain performance. Nevertheless, when implementing an approximate computing technique, quality deteriorations may appear. In order to check that the application Quality of Service is still met despite the induced approximations, several metrics can be used. The proposed method introduces an algorithm-level approximate computing method in a stereovision algorithm. The proposed algorithm-level approximation aims at reducing the computational load in a stereo matching algorithm that outputs a depth map from two rectified images. Based on a smart loop perforation technique, this method offers an interesting qual-ity/complexity trade-off. However, when comparing the obtained results to a more basic approximation technique, the results show that the quality/computation time trade-off is strongly dependent on the metric used. Our paper presents the impact of the choice of the quality metric on the results of the proposed approximate computing technique.
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hal-01879595 , version 1 (24-09-2018)



Justine Bonnot, Karol Desnos, Daniel Ménard. Algorithm-level Approximation for Fast (or not) Embedded Stereovision Algorithm. SAMOS: International Conference on Embedded Computer Systems: Architectures, MOdeling and Simulation, Jul 2018, Samos Island, Greece. ⟨10.1145/3229631.3229638⟩. ⟨hal-01879595⟩
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