Design and VLSI Implementation of Multilayered Neural Network Architecture Using Parallel Processing and Pipelining Algorithm for Image Compression

Murali Mohan*, Sathyanarayana**
* Associate Professor, Department of ECE, Sri Venkateswara College of Engineering & Technology, Chittoor, A.P, India.
** Professor, Department of ECE, College of Engineering, S.V. University, Tirupati, A.P, India.
Periodicity:January - March'2014
DOI : https://doi.org/10.26634/jse.8.3.2808

Abstract

In this paper, an optimized high speed parallel processing architecture with pipelining for multilayer neural network for image compression and decompression is implemented on FPGA (Field-Programmable Gate Array). The multilayered feed forward neural network architecture is trained using 20 sets of image data based to obtain the appropriate weights and biases that are used to construct the proposed architecture. Verilog code developed is simulated using ModelSim for verification. The FPGA implementation is carried out using Xilinx ISE 10.1. The implementation is performed on Virtex-5 FPGA board. Once interfacing is done, the corresponding programming file for the top module is generated. The target device is then configured, programming file is generated and can be successfully dumped on Virtex-5. The design is then analyzed using Chip Scope Pro. The Chip Scope output is observed. The output is successfully compared with VCS (Verliog Compiler Simulator) simulation output. The design is optimized for power of 1.01485 W and memory of 540916 KB.

Keywords

Parallel Processing, Pipelining, Multilayered Neural Network, Image Compression, Decompression, Feed

How to Cite this Article?

Sriramula,M,M., and Pabbisetti,S. (2014). Design and VLSI Implementation of Multilayered Neural Network Architecture Using Parallel Processing and Pipelining Algorithm for Image Compression. i-manager’s Journal on Software Engineering, 8(3),13-25. https://doi.org/10.26634/jse.8.3.2808

References

[1]. R. D. Dony, S. Haykin, (1995). "Neural Network Approaches to Image Compression", Proceedings of IEEE, Vol. 83, No. 2, pp. 288-303, February.
[2]. Ivan Vilovic, (2006). “An Experience in Image Compression Using Neural Networks”, 48th International Symposium ELMAR-2006, pp:95-98, June.
[3]. J. Jiang, (1999). "Image Compression with Neural Networks - A Survey", Signal Processing. Image Communication, Vol. 14, pp. 737-760, May.
[4]. H. Nait Charif and Fathi. M. Salam, (2001). “Neural Networks-based Image Compression System”, Proc. 43rd IEEE Mdwest Symp. on Circuits and Systems, Lansing MI, Vol.8, No.11, pp:846-849, Aug.
[5]. Veisi H., Jamzad M., (2005). “Image Compression Using Neural Networks, Image Processing and Machine Vision Conference (MVIP)”, Vol.14, No.9, pp.737-760, Dec.
[6]. N.Sonehara, M.Kawato, S.Miyake, K.Nakane, (1989). “Image compression using a neural network model”, International Joint Conference on Neural Networks, Vol.8, No.2, pp.288-303, Jan.
[7]. G.L. Sicuranza, G. Ramponi, S. Marsi, (1990). “Artificial neural network for image compression”, Electronics letters, Vol. 26, pp.477-479, Nov.
[8]. S. Marsi, G. Ramponi, G. L. Sicuranza, (1991). “Improved neural structure for image compression”, Proceeding of International Conference on Acoustic Speech and Signal Processing, pp.156-162, Feb.
[9]. S. Carrato, G. Ramponi, (1991). “Improved structures based on neural networks for image compression”, IEEE Workshop on Neural Networks for Signal Processing, pp.58-70, September.
[10]. S. Carrato, S. Marsi, (1992). “Compression of subband-filterd images via neural networks”, IEEE Workshop on Neural Networks for Signal Processing. pp.1228-1237, August .
[11]. G. Qiu, M. Varley, T. Terrel, (1993). “Image compression by edge pattern learning using multilayer perceptron”, Electronic letters, Vol 29, No 7, pp. 127-139, April.
[12]. R.Sentiono, G. Lu, (1994). “Image compression using a feedforward neural network”, International Conference on Neural Networks, pp.43-51, Nov.
[13]. J. Jiang, (1999). “Image compression with neural networks -A survey”, Image Communication, ELSEVIER, Vol. 14, No. 9, pp.713-722, June.
[14]. C. Cramer, (1998). “Neural networks for image and video compression: A review”, European Journal of Operational Research, Vol. 108, No.12, pp.354-369, July .
[15]. B. Verma, M. Blumenstein, and S. Kulkarni, (1997). “A Neural Network Based Technique for Data Compression”, Proceedings of the IASTED International Conference on Modelling and Simulation, pp.724-738, Aug.
[16]. A.Namphol, S.Chin, M. Arozullah, (1996). “Image compression with a hierarchical neural network”, IEEE Trans. Aerospace Electronic Systems, Vol. 32, No.1, pp.189-197, January.
[17]. J. S. Lin, S.H. Liu, (1999). “A competitive continuous Hopfield neural network for vector quantization in image compression”, Engineering Applications of Artificial Intelligence, Vol. 12, No.5, pp.210-219, Mar.
[18]. G. Pavlidis, A. Tsompanopoulos, A. Atsalakis, N. Papamarkos, C. Chamzas, (2001). “A Vector Quantization – Entropy Coder Image Compression System, IX Spanish Symposium on Pattern Recognition and Image Processing”, pp.345-355, June.
[19]. C. Amerijckx, J. D. Legaty, M. Verleysenz, (2003). “Image Compression Using Self-Organizing Maps”, Systems Analysis Modeling Simulation, Vol. 43, No. 11, pp.138-147, Nov.
[20]. S. Costa, S. Fiori, (2001). “Image compression using principal component neural networks”, Image and vision computing, Vol. 19, No.9, pp. 245-257, October.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.