Power-efficient algorithms for autonomous navigation
Y. V. Pant, H. Abbas, K.N. Nischal, P. Kelkar, D. Kumar, J. Devietti, R. Mangharam

Citation
Y. V. Pant, H. Abbas, K.N. Nischal, P. Kelkar, D. Kumar, J. Devietti, R. Mangharam. "Power-efficient algorithms for autonomous navigation". IEEE, 9, November, 2015.

Abstract
Real-time navigation of autonomous vehicles requires the processing of a large amount of sensor data by the perception algorithms onboard the vehicle, like object detection and localization. To meet the driving performance and safety requirements, these algorithms require the hardware to be over engineered to always operate for the worst-case. This leads to excessive power consumption by the computation platform. In this paper, we study how platform-level optimizations affect the computation throughput and power, and how to use this trade-off to save computation power without overly degrading throughput and control performance. The approach uses an offline profiling stage of the perception algorithm, which gives us Throughput versus Power curves for various processor frequencies and various scheduling of the perception code on CPU and GPU. At runtime, we combine power and throughput into one objective function, and design a supervisor what will determine the frequency and CPU/GPU allocation to maximize the objective. We illustrate our approach on a scaled down autonomous car which uses Vanishing Point navigation. Experimental results demonstrate that we can achieve an energy savings of upto 20% while degrading control performance by less than 1%.

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Citation formats  
  • HTML
    Y. V. Pant, H. Abbas, K.N. Nischal, P. Kelkar, D. Kumar, J.
    Devietti, R. Mangharam. <a
    href="http://www.terraswarm.org/pubs/723.html"
    >Power-efficient algorithms for autonomous
    navigation</a>, IEEE, 9, November, 2015.
  • Plain text
    Y. V. Pant, H. Abbas, K.N. Nischal, P. Kelkar, D. Kumar, J.
    Devietti, R. Mangharam. "Power-efficient algorithms for
    autonomous navigation". IEEE, 9, November, 2015.
  • BibTeX
    @inproceedings{PantAbbasNischalKelkarKumarDeviettiMangharam15_PowerefficientAlgorithmsForAutonomousNavigation,
        author = {Y. V. Pant and H. Abbas and K.N. Nischal and P.
                  Kelkar and D. Kumar and J. Devietti and R.
                  Mangharam},
        title = {Power-efficient algorithms for autonomous
                  navigation},
        booktitle = {IEEE},
        day = {9},
        month = {November},
        year = {2015},
        abstract = {Real-time navigation of autonomous vehicles
                  requires the processing of a large amount of
                  sensor data by the perception algorithms onboard
                  the vehicle, like object detection and
                  localization. To meet the driving performance and
                  safety requirements, these algorithms require the
                  hardware to be over engineered to always operate
                  for the worst-case. This leads to excessive power
                  consumption by the computation platform. In this
                  paper, we study how platform-level optimizations
                  affect the computation throughput and power, and
                  how to use this trade-off to save computation
                  power without overly degrading throughput and
                  control performance. The approach uses an offline
                  profiling stage of the perception algorithm, which
                  gives us Throughput versus Power curves for
                  various processor frequencies and various
                  scheduling of the perception code on CPU and GPU.
                  At runtime, we combine power and throughput into
                  one objective function, and design a supervisor
                  what will determine the frequency and CPU/GPU
                  allocation to maximize the objective. We
                  illustrate our approach on a scaled down
                  autonomous car which uses Vanishing Point
                  navigation. Experimental results demonstrate that
                  we can achieve an energy savings of upto 20% while
                  degrading control performance by less than 1%.},
        URL = {http://terraswarm.org/pubs/723.html}
    }
    

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