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FPGA implementation of logarithmic versions of Baum-Welch and Viterbi algorithms for reduced precision hidden Markov models

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EN
Abstrakty
EN
This paper presents a programmable system-on-chip implementation to be used for acceleration of computations within hidden Markov models. The high level synthesis (HLS) and “divide-and-conquer” approaches are presented for parallelization of Baum-Welch and Viterbi algorithms. To avoid arithmetic underflows, all computations are performed within the logarithmic space. Additionally, in order to carry out computations efficiently – i.e. directly in an FPGA system or a processor cache – we postulate to reduce the floating-point representations of HMMs. We state and prove a lemma about the length of numerically unsafe sequences for such reduced precision models. Finally, special attention is devoted to the design of a multiple logarithm and exponent approximation unit (MLEAU). Using associative mapping, this unit allows for simultaneous conversions of multiple values and thereby compensates for computational efforts of logarithmic-space operations. Design evaluation reveals absolute stall delay occurring by multiple hardware conversions to logarithms and to exponents, and furthermore the experiments evaluation reveals HMMs computation boundaries related to their probabilities and floating-point representation. The performance differences at each stage of computation are summarized in performance comparison between hardware acceleration using MLEAU and typical software implementation on an ARM or Intel processor.
Rocznik
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935--946
Opis fizyczny
Bibliogr. 33 poz., rys., wykr., tab.
Twórcy
autor
autor
  • Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, 49 Żołnierska Street, Szczecin, Poland
Bibliografia
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  • [7] A. Mannini, V. Genovese, and A. M. Sabatini, “Online decoding of hidden Markov models for gait event detection using footmounted gyroscopes”, IEEE Journal of Biomedical and Health Informatics 18 (4), 1122‒1130, 2014. doi 10.1109/jbhi.2013.2293887
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  • [19] F.L. Vargas, R.D.R. Fagundes, and D.B. Junior, “A FPGA-based Viterbi algorithm implementation for speech recognition systems”, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’01) 2, 1217‒1220, 2001. doi 10.1109/ICASSP.2001.941143
  • [20] Y. Sun, P. Li, G. Gu, Y. Wen, Y. Liu, and D. Liu, “Accelerating HMMer on FPGAs using systolic array based architecture”, Proceedings of the 23rd IEEE International Parallel and Distributed Processing Symposium (IPDPS ‘09), 2009. doi 10.1109/ipdps.2009.5160927
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  • [26] A.T. Anisha and C. Sunitha, “A hybrid Parts Of Speech tagger for Malayalam language”, International Conference on Advances in Computing, Communications and Informatics, 1502‒1507, Kochi, 2015. doi 10.1109/ICACCI.2015.7275825
  • [27] T.P. Mann, “Numerically stable hidden Markov model implementation”, HMM scaling tutorial, 1‒8, 2006.
  • [28] O. Vinyals and G. Friedland, “A Hardware-independent fast logarithm approximation with adjustable accuracy”, IEEE International Symposium on Multimedia, 61‒65. Berkeley, 2008. doi 10.1109/ISM.2008.83
  • [29] U. Dhawan and A. DeHon, “Area-efficient near-associative memories on FPGAs”, ACM Transactions on Reconfigurable Technology and Systems 7 (4), 30‒41, 2015. doi 10.1145/2435264.2435298
  • [30] D. Knuth, “The art of computer programming”, Section 5.2.4: Sorting by Merging. Sorting and Searching. 2nd ed. Addison-Wesley. 158–168, 1998.
  • [31] Jun Li, Shuangping Chen, and Yanhui Li. “The fast evaluation of hidden Markov models on GPU”, IEEE International Conference on Intelligent Computing and Intelligent Systems, 426‒430, Shanghai, 2009. doi 10.1109/icicisys.2009.5357649
  • [32] L. Yu, Y. Ukidave, and D. Kaeli, “GPU-Accelerated HMM for speech recognition”, 43rd International Conference on Parallel Processing Workshops, Minneapolis, 395‒402, 2014. doi 10.1109/ICPPW.2014.59
  • [33] M. Pietras, “Hidden Markov models with affix based observation in the field of syntactic analysis”, In: Hard and Soft Computing for Artificial Intelligence, Multimedia and Security, S. Kobayashi, A. Piegat, J. Pejaś, I. El Fray, J. Kacprzyk J. (eds) 534, 17‒26, Springer, Cham, 2016. doi 10.1007/978‒3‒319‒48429‒7_2
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b059834c-bd26-4621-8de4-b3ef572b5900
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