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Issue No.12 - December (2011 vol.60)
pp: 1678-1691
Todor Mladenov , Gwangju Institute of Science and Technology, Gwangju
Saeid Nooshabadi , Michigan Technological University, Houghton
Kiseon Kim , Gwangju Institute of Science and Technology, Gwangju
ABSTRACT
Raptor codes have been proven very suitable for mobile broadcast and multicast multimedia content delivery, and yet their computational complexity has not been investigated in the context of embedded systems. At the heart of Raptor codes are the matrix inversion and vector decoder operations. This paper analyzes the performance, energy profile, and resource implication of two matrix inversion and decoding algorithms; Gaussian elimination (GE) and third Generation Partnership Group (3GPP) standard (SA), for the Raptor decoder on a system on a chip (SoC) platform with a soft-core embedded processor. We investigate the effect of the cache size, memory type, and mapping on the performance of the two algorithms under consideration. We show that with an appropriate data to memory mapping, a speedup factor of 5.77 can be obtained for GE with respect to SA. This paper also proposes a dedicated peripheral hardware block that achieves 5.90 times better performance compared with the software, requiring an energy consumption that is lower by a factor of 5.5, when the symbol size and the data path word length are small (32 bits). We show that with parallel processing in hardware, using the wider word lengths, and employing bigger symbol sizes T, we can improve the performance, while reducing the energy consumption. Extending the hardware word length and symbol size T to 128 bits will result in a performance improvement factor of 6.73 in favor of the hardware; while energy consumption reduces by a factor of 3.8.
INDEX TERMS
Raptor codes, decoder, sparse matrix, hardware/software codesign, system on a chip, embedded system.
CITATION
Todor Mladenov, Saeid Nooshabadi, Kiseon Kim, "Implementation and Evaluation of Raptor Codes on Embedded Systems", IEEE Transactions on Computers, vol.60, no. 12, pp. 1678-1691, December 2011, doi:10.1109/TC.2010.210
REFERENCES
[1] A. Shokrollahi, “Raptor Codes,” IEEE Trans. Information Theory, vol. 52, no. 6, pp. 2551-2567, June 2006.
[2] D.J.C. Mackay, “Fountain Codes,” IEE Proc. Comm., vol. 152, no. 6, pp. 1062-1068, Dec. 2005.
[3] J. Byers, M. Luby, and M. Mitzenmacher, “A Digital Fountain Approach to Asynchronous Reliable Multicast,” IEEE J. Selected Areas in Comm., vol. 20, no. 8, pp. 1528-1540, Oct. 2002.
[4] M. Luby, “LT Codes,” Proc. 43rd Ann. IEEE Symp. Foundations of Computer Science, pp. 271-280, Nov. 2002.
[5] M. Luby, M. Watson, T. Gasiba, T. Stockhammer, and W. Xu, “Raptor Codes for Reliable Download Delivery in Wireless Broadcast Systems,” Proc. Third IEEE Consumer Comm. and Networking Conf., vol. 1, pp. 192-197, Jan. 2006.
[6] M. Luby, T. Gasiba, T. Stockhammer, and M. Watson, “Reliable Multimedia Download Delivery in Cellular Broadcast Networks,” IEEE Trans. Broadcasting, vol. 53, no. 1, pp. 235-246, Mar. 2007.
[7] T. Gasiba, T. Stockhammer, and W. Xu, “Reliable and Efficient Download Delivery with Raptor codes,” Proc. Fourth Int'l Symp. Turbo Codes and Related Topics, Apr. 2006.
[8] 3GPP TS 26.346, Technical Specification Group Services and System Aspects; Multimedia Broadcast/Multicast Service (MBMS); Protocols and Codecs, 3GPP Technical Specification, Rev. V7.4.1, June 2007.
[9] Digital Video Broadcasting (DVB); IP Datacast over DVB-H: Content Delivery Protocols, ETSI Technical Specification, Rev. V1.2.1, 2006.
[10] N. Elarief and B. Bose, “Diversity Combining ARQ over the m($\ge 2$ )-Ary Unidirectional Channel,” IEEE Trans. Computers, vol. 58, no. 8, pp. 1026-1034, Aug. 2009.
[11] P. Cataldi, M.P. Shatarski, M. Grangetto, and E. Magli, “Implementation and Performance Evaluation of LT and Raptor Codes for Multimedia Applications,” Proc. IEEE Int'l Conf. Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), Dec. 2002.
[12] O. Etesami and A. Shokrollahi, “Raptor Codes on Binary Memoryless Symmetric Channels,” IEEE Trans. Information Theory, vol. 52, no. 5, pp. 2033-2051, May 2006.
[13] Z. Cheng, J. Castura, and Y. Mao, “On the Design of Raptor Codes for Binary-Input Gaussian Channels,” IEEE. Trans. Comm., vol. 57, no. 11, pp. 3269-3277, Nov. 2009.
[14] K. Hu, J. Castura, and Y. Mao, “Reduced-Complexity Decoding of Raptor Codes over Fading Channels,” Proc. IEEE Global Telecomm. Conf. (GLOBECOM), pp. 1-5, Nov. 2006.
[15] K. Hu, J. Castura, and Y. Mao, “Performance-Complexity Tradeoffs of Raptor Codes over Gaussian Channels,” IEEE Comm. Letters, vol. 11, no. 4, pp. 343-345, Apr. 2007.
[16] J. Heo, S. Kim, J. Kim, and J. Kim, “Low Complexity Decoding for Raptor Codes for Hybrid-ARQ Systems,” IEEE Trans. Consumer Electronics, vol. 54, no. 2, pp. 390-395, May 2008.
[17] A.A. Hussein, A. Oka, and L. Lampe, “Decoding with Early Termination for Raptor Codes,” IEEE Comm. Letters, vol. 12, no. 6, pp. 444-446, June 2008.
[18] S. Kim, S. Lee, and S.-Y. Chung, “An Efficient Algorithm for ML Decoding of Raptor Codes over the Binary Erasure Channel,” IEEE Comm. Letters, vol. 12, no. 8, pp. 578-580, Aug. 2008.
[19] X. Yuan and L. Ping, “Quasi-Systematic Doped LT Codes,” IEEE J. Selected Areas in Comm., vol. 27, no. 6, pp. 866-875, Aug. 2009.
[20] S. Lin and D.J. Costello, Error Control Coding. Prentice Hall, 2004.
[21] R.E. Blahut, Algebraic Codes for Data Transmission. Cambridge Univ. Press, 2003.
[22] W.H. Press, Numerical Recipes: The Art of Scientific Computing. Cambridge Univ. Press, 2007.
[23] D. Abuaiadh, Y. Ossia, E. Petrank, and U. Silbershtein, “An Efficient Parallel Heap Compaction Algorithm,” ACM SIGPLAN Notices, vol. 39, no. 10, pp. 224-236, Oct. 2004.
[24] J. Hennessy and D. Patterson, Computer Architecture: A Quantitative Approach. Morgan Kaufman Publishers, 2003.
[25] C. Kulkarni, C. Ghez, M. Miranda, F. Catthoor, and H. De Man, “Cache Conscious Data Layout Organization for Conflict Miss Reduction in Embedded Multimedia Applications,” IEEE Trans. Computers, vol. 54, no. 1, pp. 76-81, Jan. 2005.
[26] F. Poletti, A. Poggiali, D. Bertozzi, L. Benini, P. Marchal, M. Loghi, and M. Poncino, “Energy-Efficient Multiprocessor Systems-on-Chip for Embedded Computing: Exploring Programming Models and Their Architectural Support,” IEEE Trans. Computers, vol. 56, no. 5, pp. 606-621, May 2007.
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