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}
  }
  

Posted by Elizabeth Coyne on 28 Jan 2016.
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