[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

Design of reconfigurable array processor for multimedia application

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

With the rapid growth of the amount of computations and power consumption, there is a pressing need for a high power-efficiency architecture, which takes account of computational efficiency and flexibility of application. This paper proposes a type of array-processor architecture for multimedia application which is programmable and self-reconfigurable and consists of 1024 thin-core processing elements (PE). The performance and power dissipation are demonstrated with different multimedia application algorithms such as hash, and fractional motion estimation (FME). The results show that the proposed architecture can provide high performance with less energy consumption using parallel computation.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Achuan W, Haitao C (2009) Robust perceptual image hashing based on discrete cosine transform. Chin J Saf Sci 19(4):91–96

    Google Scholar 

  2. Afonso V, Maich H, Agostini L, Franco D (2013) Low cost and high throughput FME interpolation for the HEVC emerging video coding standard. In Circuits and Systems (LASCAS), 2013 IEEE Fourth Latin American Symposium on (pp. 1–4). IEEE

  3. Chang X, Ma Z, Yang Y, Zeng Z, Hauptmann AG (2017) Bi-level semantic representation analysis for multimedia event detection. IEEE Trans Cybern 47(5):1180–1197. https://doi.org/10.1109/TCYB.2016.2539546

    Article  Google Scholar 

  4. Chang X, Ma Z, Lin M, Yang Y, Hauptmann A (2017) Feature interaction augmented sparse learning for fast kinect motion detection. IEEE Trans Image Process 26(8):3911–3920

    Article  MathSciNet  Google Scholar 

  5. Chen T C, Chen Y H, Tsai C Y, Chen L G (2006) Low power and power aware fractional motion estimation of H. 264/AVC for mobile applications. In Circuits and Systems, 2006. ISCAS 2006. Proceedings. 2006 IEEE International Symposium on (pp. 4-pp). IEEE

  6. Cheng G, Zhou P, Han J (2016) Learning rotation -invariant convolutional neural networks for object detection in vhr optical remote sensing images. IEEE Trans Geosci Remote Sens 54(12):7405–7415

    Article  Google Scholar 

  7. Cheng, G., Han, J., & Lu, X. (2017). Remote sensing image scene classification: benchmark and state of the art. Proceedings of the IEEE. to be published. https://doi.org/10.1109/JPROC.2017.2675998

    Article  Google Scholar 

  8. Coding H E V (2013) Recommendation ITU-T H. 265. International Standard ISO/IEC, 23008–2

  9. Feng Y, Ren J, Jiang J (2011) Object-based 2d-to-3d video conversion for effective stereoscopic content generation in 3d-tv applications. IEEE Trans Broadcast 57(2):500–509

    Article  Google Scholar 

  10. Fridrich J, Goljan M (2000) Robust hash functions for digital watermarking. In Information Technology: Coding and Computing, 2000. Proceedings. International Conference on (pp. 178–183). IEEE

  11. Han J, Pauwels EJ, de Zeeuw P (2013) Fast saliency-aware multi-modality image fusion. Neurocomputing 111(6):70–80

    Article  Google Scholar 

  12. Han J, Zhang D, Cheng G, Guo L, Ren J (2015) Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning. IEEE Trans Geosci Remote Sens 53(6):3325–3337

    Article  Google Scholar 

  13. Jafri SMAH, Daneshtalab M, Abbas N, Leon GS, Hemani A (2016) Transmap: transformation based remapping and parallelism for high utilization and energy efficiency in cgras. IEEE Trans Comput 65(11):3456–3469

    Article  MathSciNet  Google Scholar 

  14. Jiang J, Kohler J, Williams C, Zaletelj J, Guntner G, Horstmann H et al (2011) Live: An integrated production and feedback system for intelligent and interactive tv broadcasting. IEEE Trans Broadcast 57(3):646–661

    Article  Google Scholar 

  15. Junyong D, Lin J, Jixing C (2016) An energy efficiency-driven programmable and self-reconfigurable architecture of mobile graphics processor. Available: http://or.nsfc.gov.cn/bitstream/00001903-5/158408/1/1000014273395.pdf

  16. Kailasanathan C, Naini R S (2001) Image authentication surviving acceptable modifications using statistical measures and k-mean segmentation. IEEE-EURASIP Work. Nonlinear Sig. and Image Processing, 1

  17. Kim Y, Joo H, Yoon S (2016) Inter-coarse-grained reconfigurable architecture reconfiguration technique for efficient pipelining of kernel-stream on coarse-grained reconfigurable architecture-based multicore architecture. Iet Circ Devices Syst 10(4):251–265

    Article  Google Scholar 

  18. León J S, Cárdenas C S, Castillo E V (2016) A high parallel HEVC Fractional Motion Estimation architecture. In ANDESCON, 2016 IEEE (pp. 1–4). IEEE

  19. Li H, Zhang Y, Chao H (2013). An optimally scalable and cost-effective fractional-pixel motion estimation algorithm for HEVC. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on (pp. 1399–1403). IEEE

  20. Ma Z, Chang X, Yang Y, Sebe N, Hauptmann AG (2017) The many shades of negativity. IEEE Trans Multimed 19(7):1558–1568

    Article  Google Scholar 

  21. Maich H, Afonso V, Franco D, Zatt B, Porto M, Agostini L (2013) High throughput hardware design for the HEVC fractional motion estimation interpolation unit. In Electronics, Circuits, and Systems (ICECS), 2013 IEEE 20th International Conference on (pp. 161–164). IEEE

  22. Odamaki M, Nayar SK 2016, Cambits: A Reconfigurable Camera System. Department of Computer Science, Columbia University, New York

  23. Pastuszak G, Trochimiuk M (2016) Architecture design of the high-throughput compensator and interpolator for the H. 265/HEVC encoder. J Real-Time Image Proc 11(4):663–673

    Article  Google Scholar 

  24. Pellauer M, Parashar A, Adler M, Ahsan B, Allmon R, Crago N et al (2015) Efficient control and communication paradigms for coarse-grained spatial architectures. ACM Trans Comput Syst 33(3):10

    Article  Google Scholar 

  25. Qian X, Wang H, Zhao Y, Hou X, Hong R, Wang M, Tang YY (2017) Image location inference by multisaliency enhancement. IEEE Trans Multimedia 19(4):813–821

    Article  Google Scholar 

  26. Ren J, Jiang J (2009) Hierarchical modeling and adaptive clustering for real-time summarization of rush videos. IEEE Trans Multimed 11(5):906–917

    Article  Google Scholar 

  27. Ren J, Jiang J, Chen J (2009) Shot boundary detection in mpeg videos using local and global indicators. IEEE Trans Circ Syst Video Technol 19(8):1234–1238

    Article  Google Scholar 

  28. Sullivan GJ, Ohm J, Han WJ, Wiegand T (2012) Overview of the high efficiency video coding (HEVC) standard. IEEE Trans Circ Syst Video Technol 22(12):1649–1668

    Article  Google Scholar 

  29. Wei S, Liu L, Yin S (2013) Reconfigurable computing processor technology. China Sci Inf Sci 42(12):1559–1576

    Google Scholar 

  30. Yao X, Han J, Guo L, Bu S, Liu Z (2015) A coarse-to-fine model for airport detection from remote sensing images using target-oriented visual saliency and CRF. Neurocomputing 164(C):162–172

    Article  Google Scholar 

  31. Yao X, Han J, Cheng G, Qian X, Guo L (2016) Semantic annotation of high-resolution satellite images via weakly supervised learning. IEEE Trans Geosci Remote Sens 54(6):3660–3671

    Article  Google Scholar 

  32. Yao X, Han J, Zhang D, Nie F (2017) Revisiting Co-Saliency Detection: A Novel Approach Based on Two-Stage Multi-View Spectral Rotation Co-clustering. IEEE Trans Image Process 26(7):3196–3209

    Article  MathSciNet  Google Scholar 

  33. Ye L, Pan Y, Rongkai X, Di L, Jian Y (2016) FP-CNNH:, A fast image hashing algorithm based on depth convolution neural network. Comput Sci 43(9):39–46

    Google Scholar 

  34. You F, Peng L (2008) Fast searching algorithm for integer and fractional pixels in H.264. J Zhejiang Univ Eng Ed 42(2):332–336

    MathSciNet  Google Scholar 

  35. Zabalza J, Ren J, Ren J, Liu Z, Marshall S (2014) Structured covariance principal component analysis for real-time onsite feature extraction and dimensionality reduction in hyperspectral imaging. Appl Opt 53(20):4440–4449

    Article  Google Scholar 

  36. Zabalza J, Ren J, Zheng J, Han J, Zhao H, Li S, Marshall S (2015) Novel two-dimensional singular spectrum analysis for effective feature extraction and data classification in hyperspectral imaging. IEEE Trans Geosci Remote Sens 53(8):4418–4433

    Article  Google Scholar 

  37. Zhang Q, Liu Y, Blum R S, Han J, Tao D (2017) Sparse representation based multi-sensor image fusion: a review. [online] Available: https://arxiv.org/abs/1702.03515

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China (61272120, 61602377, and 61634004), the Natural Science Foundation of Shaanxi Province of China (2015JM6326), Shaanxi Provincial Co-ordination Innovation Project of Science and Technology (2016KTZDGY02-04-02), and the Project of Education Department of Shaanxi Provincial Government (15JK1683).

Author information

Authors and Affiliations

  1. School of microelectronics, Xidian University, Xi’an, Shaanxi, 710071, China

    Zhu Yun

  2. School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an, 710121, China

    Lin Jiang, Shuai Wang & Hui Song

  3. College of Computer and Information Science, Northeastern University, Boston, MA, 02115, USA

    Xingjie Huang

  4. School of Computer Science, Xi’an University of Posts and Telecommunications, Xi’an, 710121, China

    Xueting Li

Authors
  1. Zhu Yun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lin Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shuai Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xingjie Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hui Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xueting Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lin Jiang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yun, Z., Jiang, L., Wang, S. et al. Design of reconfigurable array processor for multimedia application. Multimed Tools Appl 77, 3639–3657 (2018). https://doi.org/10.1007/s11042-017-5284-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-017-5284-7

Keywords

Search

Navigation