FPGA implementation of particle swarm optimization for Bayesian network learning

Computers and Electrical Engineering

Volumn 39, Issue 8, 2013, Pages 2454-2468

  Hibbard, Matthew J ^a   Peskin, Eric R ^b   Sahin, Ferat ^c  

^a SYRACUSE RESEARCH CORPORATION   (United States)

^b NEW YORK UNIVERSITY MEDICAL CENTER   (United States)

^c Kate Gleason College of Engineering   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORK LEARNING; CONDITIONAL PROBABILITY TABLES; FITNESS EVALUATIONS; FLOATING-POINT CALCULATIONS; FPGA IMPLEMENTATIONS; FPGA-BASED IMPLEMENTATION; RECONFIGURABLE COMPUTING; SOFTWARE IMPLEMENTATION;

BAYESIAN NETWORKS; FIELD PROGRAMMABLE GATE ARRAYS (FPGA); HEALTH; RECONFIGURABLE ARCHITECTURES;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 84888643923     PISSN: 00457906     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compeleceng.2013.07.018     Document Type: Conference Paper

References (27)

1. F.V. Jensen, and T.D. Nielsen Bayesian networks and decision graphs 2nd ed. Information science and statistics 2007 Springer Science + Business Media, LLC New York (NY, USA) ISBN: 978-0-387-68281-5
2. F. Sahin, Mc Yavuz, Z. Arnavut, and O. Uluyol Fault diagnosis for airplane engines using Bayesian networks and distributed particle swarm optimization Parallel Comput 33 2 2007 124 143 http://dx.doi.org/10.1016/j. parco.2006.11.005
3. Chickering DM, Geiger D, Heckerman D. Learning Bayesian networks is NP-hard. Tech. Rep. MSR-TR-94-17; Microsoft Research; 1994.
4. D. Heckerman, D. Geiger, and D.M. Chickering Learning Bayesian networks: the combination of knowledge and statistical data Mach Learn 20 3 1995 197 243
5. P. Larrañaga, M. Poza, Y. Yurramendi, R.H. Murga, and C.M.H. Kuijpers Structure learning of Bayesian networks by genetic algorithms: a performance analysis of control parameters IEEE Trans Pattern Anal Mach Intell 18 9 1996 912 926
6. Kennedy J, Eberhart R. Particle swarm optimization. In: 1995. Proceedings. IEEE International conference on neural networks, vol. 4; 1995. p. 1942-8.
7. A. Salman, I. Ahmad, and S. Al-Madani Particle swarm optimization for task assignment problem Microprocess Microsyst 26 8 2002 363 371 http://dx.doi.org/10.1016/S0141-9331(02)00053-4
8. Yavuz MC, Sahin F, Arnavut Z, Uluyol O. Generating and exploiting Bayesian networks for fault diagnosis in airplane engines. In: 2006 IEEE international conference on granular computing; 2006, p. 250-5.
9. J.E. Vuillemin, P. Bertin, D. Roncin, M. Shand, H.H. Touati, and P. Boucard Programmable active memories: reconfigurable systems come of age IEEE Trans VLSI Syst 4 1 1996 56 69
10. K. Compton, and S. Hauck Reconfigurable computing: a survey of systems and software ACM Comput Surv 34 2 2002 171 210
11. S. Hauck, and A. DeHon Reconfigurable computing: the theory and practice of FPGA-based computation Systems on Silicon 2007 Morgan Kaufmann ISBN: 0123705223
12. Pournara I, Bouganis CS, Constantinides GA. FPGA-accelerated Bayesian learning for reconstruction of gene regulatory networks. In: International conference on field programmable logic and applications; 2005. p. 323-8.
13. Reynolds PD, Duren RW, Trumbo ML, Marks RJ. FPGA implementation of particle swarm optimization for inversion of large neural networks. In: Proceedings 2005 IEEE Swarm intelligence symposium, 2005. SIS 2005; 2005. p. 389-92.
14. N.B. Asadi, T.H. Meng, and W.H. Wong Reconfigurable computing for learning Bayesian networks FPGA '08: proceedings of the 16th international ACM/SIGDA symposium on field programmable gate arrays 2008 ACM New York (NY, USA) 203 211 ISBN: 978-1-59593-934-0, http://doi.acm.org/10.1145/1344671.1344702
15. N.B. Asadi, C.W. Fletcher, G. Gibeling, E.N. Glass, K. Sachs, and D. Burke ParaLearn: a massively parallel, scalable system for learning interaction networks on FPGAs Proceedings of the 24th ACM international conference on supercomputing. ICS '10 2010 ACM New York (NY, USA) 83 94 ISBN: 978-1-4503-0018-6, http://doi.acm.org/10.1145/1810085.1810100
16. M.D. Linderman, R. Bruggner, V. Athalye, T.H. Meng, N.B. Asadi, and G.P. Nolan High-throughput Bayesian network learning using heterogeneous multicore computers Proceedings of the 24th ACM international conference on supercomputing. ICS '10 2010 ACM New York (NY, USA) 95 104 ISBN: 978-1-4503-0018-6, http://doi.acm.org/10.1145/1810085.1810101
17. Steck H. Constraint-based structural learning in Bayesian networks using finite data sets. Ph.D. thesis. Institut für Informatik der Technischen Universität München; 2001.
18. D. Heckerman, A. Mamdani, and M.P. Wellman Real-world applications of Bayesian networks Commun ACM 38 3 1995 24 26
19. Kennedy J, Eberhart RC. A discrete binary version of the particle swarm algorithm. In: 1997 IEEE international conference on systems, man, and cybernetics, 1997. Computational cybernetics and simulation. vol. 5; 1997. p. 4104-8.
20. Herskovits E. Computer-based probabilistic-network construction. Ph.D. thesis. Stanford (CA, USA): Stanford University; 1992 [UMI Order No. GAX92-05646].
21. IEEE standard for floating-point arithmetic. IEEE Computer Society; 2008 [IEEE Std 754-2008].
22. S. Warshall A theorem on Boolean matrices J ACM 9 1 1962 11 12 http://doi.acm.org/10.1145/321105.321107
23. Xilinx Core Generator System. Xilinx, Inc.; 2009. < http://www.xilinx.com/tools/coregen.htm >.
24. J. Detrey, and F. de Dinechin Parameterized floating-point logarithm and exponential functions for FPGAs Microprocess Microsyst 31 8 2007 537 545 http://dx.doi.org/10.1016/j.micpro.2006.02.008
25. W. Feller A direct proof of Stirling's formula Am Math Mon 74 10 1967 1223 1225 < http://www.jstor.org/stable/2315671 >
26. W.H. Sewell, and V.P. Shah Social class, parental encouragement, and educational aspirations Am J Sociol 73 5 1968 559 572
27. WILDCARD-II and WILDCARD-4 Reference Manual. Annapolis Micro Systems, Inc.; 2006. 12968 - 0000 Revision 3.6. < http://www.annapmicro.com/manuals/ wildcardii-4-refmanual-v3.6.pdf >.