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Polish Information Processing Society
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Annals of Computer Science and Information Systems, Volume 8

Proceedings of the 2016 Federated Conference on Computer Science and Information Systems

Formulation and Practical Solution for the Optimization of Memory Accesses in Embedded Vision Systems

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DOI: http://dx.doi.org/10.15439/2016F124

Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 609–617 ()

Full text

Abstract. The design of modern-day electronic devices carry many interesting optimization challenges, one of which is the efficient access to the image memory. For some particular cases of image treatments, Mancini and Rousseau (Proc.DATE, 2012) have proposed a software system, called Memory Management Optimization (MMOpt), that creates adhoc memory hierarchies for accelerating the accesses to the memories holding the image on which image treatments are applied. It splits input and output images into tiles, and prefetches the input tiles into local buffers for faster accesses.

References

  1. S. Mancini and F. Rousseau, “Enhancing non-linear kernels by an optimized memory hierarchy in a high level synthesis flow,” in Proceedings of the Conference on Design, Automation and Test in Europe. EDA Consortium, 2012, pp. 1130–1133.
  2. S. Mancini and F. Rousseau, “Optimisation d’accélérateurs matériels de traitement par incorporation d’un gestionnaire de données et de contrôle dans un flot de HLS,” in Conférence en Parallélisme, Architecture et Système, 2013.
  3. C. Tang and E. Denardo, “Models arising from a flexible manufacturing machine, part i: Minimization of the number of tool switches,” Operations Research, vol. 36, no. 5, pp. 767–777, 1988.
  4. Y. Crama, A. Kolen, A. Oerlemans, and F. Spieksma, “Minimizing the number of tool switches on a flexible machine,” International Journal of Flexible Manufacturing Systems, vol. 6, no. 1, pp. 33–54, 1994.
  5. J. Bard, “A heuristic for minimizing the number of tool switches on a flexible machine,” IIE Transactions, vol. 20, no. 4, pp. 382–391, 1988. http://dx.doi.org/10.1080/07408178808966195
  6. C. Privault and G. Finke, “Modelling a tool switching problem on a single nc-machine,” Journal of Intelligent Manufacturing, vol. 6, no. 2, pp. 87–94, 1995. http://dx.doi.org/10.1007/BF00123680
  7. G. Laporte, J. Salazar-Gonzalez, and F. Semet, “Exact algorithms for the job sequencing and tool switching problem,” IIE Transactions, vol. 36, no. 1, pp. 37–45, 2004. http://dx.doi.org/10.1080/07408170490257871
  8. A. Konak and S. Kulturel-Konak, “An ant colony optimization approach to the minimum tool switching instant problem in flexible manufacturing system,” in 2007 IEEE Symposium on Computational Intelligence in Scheduling, 2007.
  9. J. Amaya, C. Cotta, and A. Fernández, “A memetic algorithm for the tool switching problem,” in Hybrid metaheuristics. Springer, 2008, pp. 190–202.
  10. D. Catanzaro, L. Gouveia, and M. Labbé, “Improved integer linear programming formulations for the job sequencing and tool switching problem,” European Journal of Operational Research, vol. 244, no. 3, pp. 766–777, 2015.
  11. A. Thornton and S. Sangwine, “Log-polar sampling incorporating a novel spatially variant filter to improve object recognition,” in Sixth International Conference on Image Processing and Its Applications, vol. 2, 1997, pp. 776–779.
  12. N. Bellas, S. Chai, M. Dwyer, and D. Linzmeier, “Real-time fisheye lens distortion correction using automatically generated streaming accelerators,” in 17th IEEE Symposium on Field Programmable Custom Computing Machines, FCCM’09., 2009, pp. 149–156.
  13. P. Viola and M. Jones, “Robust real-time face detection,” International journal of computer vision, vol. 57, no. 2, pp. 137–154, 2004.
  14. D. Applegate, R. Bixby, W. Cook, and V. Chvátal, On the solution of traveling salesman problems. Rheinische Friedrich-Wilhelms-Universität Bonn, 1998.