Skip to main content
SpringerLink
Log in

Multi-constraints self-adaptation for reconfigurable multimedia embedded systems

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

The application of multimedia in embedded systems (ES), such as Virtual reality and 3-D imaging, represents the current trend in ES development. Coupling multimedia with ES has raised new multimedia-related challenges that have been added to the common ES constraints. These challenges deal with the real-time, quality, performance and efficient processing requirements of multimedia applications. The integration of self-adaptation in ES development has been, for many years, a paramount solution to cope with these issues. Although there has been extensive research on the topic of ES self-adaptation, the related works still lack global approaches that better deal with multimedia-related constraints. Coordinating different adaptation mechanisms, monitoring multiple system constraints and supporting multi-application contexts are still underexplored. The aim of the present work is to fill in these gaps by providing a global adaptation approach that offers better adaptation decisions with fair resource sharing among competing multimedia applications. With the above challenges in mind, we propose a multi-constraints combined adaptation approach that targets multimedia ES. It addresses four critical system constraints: maximizing the overall system‘s Quality of Application (QoA) under the real-time constraint, the remaining system energy and the available network bandwidth. It coordinates the adaptation at both application and architecture levels. To test and validate the proposed technique, a videophone system is designed on a Xilinx FPGA development board. It executes two complex multimedia applications. The validation results show the aptitude of the proposed system to successfully reconfigure itself at run-time in response to its constraints.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Jesus Rubio J (2021) Stability analysis of the modified Levenberg-Marquardt algorithm for the artificial neural network training. IEEE Transact Neural Netw Learn Syst 32:3510–3524

    Article  MathSciNet  Google Scholar 

  2. Jesus Rubio J, Lughofer E, Pieper J, Cruz P, Ivan Martinez D, Ochoa G, Antonio Islas M, Garcia E (2021) Adapting H-infinity controller for the desired reference tracking of the sphere position in the maglev process. Inf Sci 569:669–686

    Article  MathSciNet  Google Scholar 

  3. Chiang H-S, Chen M-Y, Huang Y-J (2019) Wavelet-based EEG processing for epilepsy detection using fuzzy entropy and associative petri net. IEEE Access 7:103255–103262

    Article  Google Scholar 

  4. Jesus Rubio1 J, Pan Y, Pieper J, Chen M, Humberto Sossa Azuela J (2021) Editorial: advances in robots trajectories learning via fast neural networks Front Neurorobot

  5. Vargas D (2021) Superpixels extraction by an Intuitionistic fuzzy clustering algorithm. J Appl Res Technol 19:140–152

    Article  Google Scholar 

  6. Soriano LA, Zamora E, Vazquez-Nicolas JM, Hernández G, Barraza Madrigal JA, Balderas D, PD (2020) Control compensation based on a cascade neural network applied to a robot manipulator. Front Neurorobot. Vol. 14

  7. Wang C, Li Z, Sarpong B (2021) Multimodal adaptive identity-recognition algorithm fused with gait perception. Big Data Min Anal 4:223–232

    Article  Google Scholar 

  8. Bi R, Liu Q, Ren J, Tan G (2021) Utility aware offloading for mobile-edge computing. Tsinghua Sci Technol 26(2):239–250

    Article  Google Scholar 

  9. Harb N, Niar S, Saghir MaR, El Hillali Y, Ben Atitallah R (2011) Dynamically reconfigurable architecture for a driver assistant system, IEEE 9th Symp Appl Specif Process., pp. 62–65

  10. Muhlbauer F, Bobda C (2006) A dynamic reconfigurable hardware/software architecture for object tracking in video streams, EURASIP J Embed Syst, pp. 1–8

  11. Boutekkouk Fateh (2020) Adaptive embedded systems: a systematic review. Int J Auton Adapt Commun Syst 13:55–83

    Article  Google Scholar 

  12. Lapotre V, Murugappa P, Gogniat G, Baghdadi A, Hubner M, Diguet JP (2015) A dynamically reconfigurable multi-ASIP architecture for multistandard and multimode turbo decoding. IEEE Trans Very Large Scale Integr Syst 24:383–387

    Article  Google Scholar 

  13. Vipin K, Fahmy SA (2014) Automated partial reconfiguration design for adaptive systems with CoPR for Zynq, IEEE 22nd International Symposium on Field-Programmable Custom Computing Machines, pp 202–205

  14. Beretta I, Rana V, Santambrogio MD, Sciuto D (2009) On-line task management for a reconfigurable cryptographic architecture, IEEE International Parallel and Distributed Processing Symposium

  15. Nolting S, Paya-Vaya G, Giesemann F, Blume H (2015) Exploring Dynamic Reconfigurable CORDIC Coprocessors Tightly Coupled with a VLIW-SIMD Soft-Processor Architecture, Lecture Notes in Computer Science, pp 401–410

  16. Milakovich A, Gopinath VS, Lysecky R, Sprinkle J (2012) Automated Software Generation and Hardware Coprocessor Synthesis for Data-Adaptable Reconfigurable Systems, IEEE 19th International Conference and Workshops on Engineering of Computer-Based Systems, pp 15–23

  17. Jiang Y, Pattichis M (2013) A dynamically reconfigurable deblocking filter for H.264/AVC codec, Asilomar Conference on Signals, Systems and Computers, pp 2189–2193

  18. Jiang Y, Pattichis M (2012) A dynamically reconfigurable DCT architecture for maximum image quality subject to dynamic power and bitrate constraints, IEEE Southwest Symposium on Image Analysis and Interpretation, pp. 189–192

  19. Lapray P-J, Heyrman B, Ginhac D (2014) HDR-ARtiSt: an adaptive real-time smart camera for high dynamic range imaging, J Real-Time Image Process

  20. Kim C, Chung M, Cho Y, Konijnenburg M, Ryu S, Kim J (2014) ULP-SRP ultra low-power Samsung reconfigurable processor for biomedical applications. ACM Trans Reconfigurable Technol Syst 7:1–15

    Google Scholar 

  21. Hamzaoglu I, Aysu A, Ulusel OC (2011) A reconfigurable H.264 video encoder hardware, IEEE 19th Signal Processing and Communications Applications Conference (SIU), pp 984–987

  22. Shaha N, Desai A, Parashar M (2001) Multimedia content adaptation for QoS management over heterogeneous networks, International Conference on Internet Computing

  23. Ngoc NP, Lafruit G, Deconinck G, Lauwereins R (2002) A Fast QoS Adaptation Algorithm for MPEG-4 Multimedia Applications, Joint International Workshops on Interactive Distributed Multimedia Systems and Protocols for Multimedia Systems: Protocols and Systems for Interactive Distributed Multimedia, pp. 92–105

  24. Vardhan V, Yuan W, Harris AF, Adve SV, Kravets RH, Nahrstedt K, Sachs DG, Jones DL (2009) GRACE-2, integrating fine-grained application adaptation with global adaptation for saving energy. Int J Embed Syst 4:152

    Article  Google Scholar 

  25. Diguet J-P, Eustache Y, Gogniat G (2011) Closed-loop-based self-adaptive Hardware/Software-Embedded systems. ACM Trans Embed Comput Syst 10:1–28

    Article  Google Scholar 

  26. Wildermann S, Reimann F, Ziener D, Teich J (2012) System level synthesis flow for self-adaptive multi-mode reconfigurable systems. Workshop on Self-Awareness in Reconfigurable Computing Systems (SRCS) 2:4–7

  27. Loukil K, Ben Amor N, Abid M, Diguet JP (2013) Self-adaptive on-chip system based on cross-layer adaptation approach, Int J Reconfigurable Comput, pp 1–17

  28. Ben Amor N, Ramzy M, Yemna B, Ghorbel A, Frikha T (2016) Design of an adaptive smart camera with dynamic reconfiguration and disembedding technique. Int J Comput Sci Inf Secur 14:650–669

    Google Scholar 

  29. Biedermann A, Huss SA, Israr A (2015) Safe dynamic reshaping of reconfigurable MPSoC embedded systems for self-healing and self-adaption purposes. ACM Trans Reconfigurable Technol Syst 8:1–26

    Article  Google Scholar 

  30. Quan W, Pimentel AD (2015) Towards self-adaptive MPSoC systems with adaptivity throttling, In: 2015 International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS). pp 157–164

  31. Ben Saïd M, Ben Amor N, Abid M, Ben Taher F, Diguet JP (2011) A bi-constraints adaptation technique for embedded multimedia systems, International Conference on Multimedia Computing and Systems

  32. National Instruments, NI smart camera, www.ni.com

  33. Suhring K (2014) H.264/AVC Software Coordination

  34. Saponara S, Melani M, Fanucci L, Terren P (2014) Adaptive algorithm for fast motion estimation in H.264/MPEG-4 AVC

  35. Elyousfi A, Tamtaoui A, Bouyakhf E (2007) A new fast intra prediction mode decision algorithm for H2.64 / AVC encoders. Int J Electr Electron Sci Eng 1(3):1–7

    Google Scholar 

  36. H . 264 Motion Estimation Engine v1 , http://www.xilinx.com/support/documentation/ip_documentation/h264_mee_ds648.pdf

  37. H.264 CABAC, http://www.xilinx.com/support/documentation/ip_documentation/h264_cabac_prodbrief_ds602.pdf

  38. Rethinagiri SK, Ben Atitallah R, Niar S, Senn E, Dekeyser JL (2011) Hybrid system level power consumption estimation for FPGA-based MPSoC, IEEE Int Conf Comput Des VLSI Comput Process, pp. 239–246

  39. Krupitzer C, Maximilian Roth F, VanSyckel S, Schiele G, Becker C (2015) A survey on engineering approaches for self-adaptive systems. Pervasive Mob Comput, pp 184–206

  40. Han DS, Yang QL, Xing JC et al (2020) EasyModel, a refinement-based modeling and verification approach for self-adaptive software. J Comput Sci Technol 35:1016–1046

    Article  Google Scholar 

  41. Nissaf F, Kacem YH, Abid M (2021) An event-based approach for formally verifying runtime adaptive real-time systems. J. Supercomput 77:3110–3143

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Embedded Systems Laboratory, National Engineering School of Sfax, University of Sfax, Sfax, Tunisia

    Mouna Ben Said, Nader Ben Amor, Fatma Ben Taher & Khaled Lahbib

Authors
  1. Mouna Ben Said
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nader Ben Amor
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fatma Ben Taher
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Khaled Lahbib
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mouna Ben Said.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ben Said, M., Ben Amor, N., Ben Taher, F. et al. Multi-constraints self-adaptation for reconfigurable multimedia embedded systems. J Supercomput 78, 9038–9064 (2022). https://doi.org/10.1007/s11227-021-04269-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-021-04269-3

Keywords

Search

Navigation