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On the Vectorization of FIR Filterbanks
EURASIP Journal on Advances in Signal Processing volume 2007, Article number: 091741 (2006)
This paper presents a vectorization technique to implement FIR filterbanks. The word vectorization, in the context of this work, refers to a strategy in which all iterative operations are replaced by equivalent vector and matrix operations. This approach allows that the increasing parallelism of the most recent computer processors and systems be properly explored. The vectorization techniques are applied to two kinds of FIR filterbanks (conventional and recursi ve), and are presented in such a way that they can be easily extended to any kind of FIR filterbanks. The vectorization approach is compared to other kinds of implementation that do not explore the parallelism, and also to a previous FIR filter vectorization approach. The tests were performed in Matlab and, in order to explore different aspects of the proposed technique.
Edelman A, McCorquodale P, Toledo S: The future fast Fourier transform? SIAM Journal on Scientific Computing 1999,20(3):1094–1114.
Frigo M, Johnson SG: FFTW: an adaptive software architecture for the FFT. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '98), May 1998, Seattle, Wash, USA 3: 1381–1384.
Thiede TV: Perceptual audio quality assessment using a non-linear filter bank, Ph.D. thesis. Technical University of Berlin, Berlin, Germany; 1999.
Weinhardt M, Luk W: Pipeline vectorization. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2001,20(2):234–248. 10.1109/43.908452
Fahringer T, Scholz B: A unified symbolic evaluation framework for parallelizing compilers. IEEE Transactions on Parallel and Distributed Systems 2000,11(11):1105–1125. 10.1109/71.888633
Blume W, Eigenmann R, Faigin K, et al.: Polaris: the next generation in parallelizing compilers. Proceedings of the 7th International Workshop in Languages and Compilers for Parallel Computing (LCPC '94), August 1994, Ithaca, NY, USA 10.1–10.18.
Zima H, Chapman B: Supercompilers for Parallel and Vector Computers. Addison-Wesley, New York, NY, USA; 1990.
Silverman HF: A high-quality digital filterbank for speech recognition which runs in real time on a standard microprocessor. IEEE Transactions on Acoustics, Speech, and Signal Processing 1986,34(5):1064–1073. 10.1109/TASSP.1986.1164947
Redmill DW, Bull DR: Design of low complexity FIR filters using genetic algorithms and directed graphs. Proceedings of the 2nd International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, September 1997, Glasgow, UK 168–173.
Soderstrand MA, Johnson LG, Arichanthiran H, Hoque MD, Elangovan R: Reducing hardware requirement in FIR filter design. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '00), June 2000, Istanbul, Turkey 6: 3275–3278.
Tan K-H, Leong WF, Kadam S, Soderstrand MA, Johnson LG: Public-domain matlab program to generate highly optimized VHDL for FPGA implementation. Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS '01), May 2001, Sydney, Australia 514–517.
Brückmann D: Optimized digital signal processing for flexible receivers. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '02), May 2002, Orlando, Fla, USA 4: 3764–3767.
Cruz-Roldán F, Monteagudo-Prim M: Efficient implementation of nearly perfect reconstruction FIR cosine-modulated filterbanks. IEEE Transactions on Signal Processing 2004,52(9):2661–2664. 10.1109/TSP.2004.831913
Sung W, Mitra SK: Implementation of digital filtering algorithms using pipelined vector processors. Proceedings of the IEEE 1987,75(9):1293–1303.
Meyer MD, Agrawal DP: Vectorization of the DLMS transversal adaptive filter. IEEE Transactions on Signal Processing 1994,42(11):3237–3240. 10.1109/78.330384
Kim D, Choe G:AMD's 3DNow vectorization for signal processing applications. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '99), March 1999, Phoenix, Ariz, USA 4: 2127–2130.
Robelly JP, Cichon G, Seidel H, Fettweis G: Implementation of recursive digital filters into vector SIMD DSP architectures. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '04), May 2004, Montreal, Canada 5: 165–168.
Van Der Horst M, Van Berkel K, Lukkien J, Mak R: Recursive filtering on a vector DSP with linear speedup. Proceedings of IEEE International Conference on Application-Specific Systems, Architectures and Processors, July 2005, Samos, Greece 379–386.
Shahbahrami A, Juurlink BHH, Vassiliadis S: Efficient vectorization of the FIR filter. Proceedings of the 16th Annual Workshop on Circuits, Systems and Signal Processing (ProRisc '05), November 2005, Veldhoven, The Netherlands 432–437.
Barbedo JGA, Lopes A: A new cognitive model for objective assessment of audio quality. Journal of the Audio Engineering Society 2005,53(1–2):22–31.
Barbedo JGA, Lopes A: A new strategy for objective estimation of the quality of audio signals. IEEE Latin-America Transactions 2004.,2(3):
ITU-R Recommendation BS-1387 : Method for Objective Measurements of Perceived Audio Quality. 1998.
Oppenheim AV, Schafer RW: Discrete-Time Signal Processing. Prentice Hall, Englewood Cliffs, NJ, USA; 1989.
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Barbedo, J.G.A., Lopes, A. On the Vectorization of FIR Filterbanks. EURASIP J. Adv. Signal Process. 2007, 091741 (2006). https://doi.org/10.1155/2007/91741
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