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A survey of software and hardware use in artificial neural networks

  • New applications of Artificial Neural Networks in Modeling & Control
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Abstract

Artificial neural networks (ANNs) have been widely used over the last three decades. During this period, many hardware and software solutions have been developed and today a new user entering the field can make a fast trial to this artificial intelligence solution with commercial software and hardware, instead of developing a solution from scratch thus saving a lot of time. This work aims at helping new and experienced users even further by sharing the ANNs experience in software and hardware collected. This was achieved through a survey questionnaire about present and past used solutions of software and hardware, as well as future prospects for the development of application areas. To further enlighten the reader, a logistic regression (LR) statistical analysis is performed on the obtained results to extract additional details about the answers obtained from the ANN community. The LR statistical analysis verifies whether the researchers with more than 25 years of experience in ANNs use self-written code when compared to those with less years of experience in the area. The LR statistical analysis also verifies whether researchers with less than 25 years of experience in ANNs use some platform to develop their hardware when compared to those who have more years of experience.

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References

  1. Emergent Neural Network Simulation System (2013). Last Accessed Feb 28 2013 Available in: http://grey.colorado.edu/emergent/index.php/Main_Page

  2. Bower JM, Beeman D (1998) The book of GENESIS: exploring realistic neural models with the GEneral NEural SImulation System, 2nd edn. Telos, New York, p 458

    Book  Google Scholar 

  3. Brette R, Rudolph M, Carnevale T, Hines M, Beeman D, Bower JM, Diesmann M, Goodman PH, Harris FCJ, Zirpe M, Natschläger T, Pecevski D, Ermentrout B, Djurfeldt M, Lansner A, Rochel O, Vieville T, Muller E, Davison A, El Boustani S, Destexhe A (2007) Simulation of networks of spiking neurons: a review of tools and strategies. J Comput Neurosci 23:349–398

    Article  MathSciNet  Google Scholar 

  4. C-A Popa, C Cernăzanu-Glăvan (2010) Pattern neural network: a case study. 2nd workshop on software services: cloud computing and applications based on software services, Timisoara, Romania

  5. Demuth H, Beale M, Hagan M (2006) Neural network toolbox for use with MATLAB. Neural Network Toolbox, MathWorks

  6. Diesmann M, Gewaltig MO, Aertsen A (1995) SYNOD: an environment for neural systems simulations. Language interface and tutorial. Tech. Rep. GC-AA-/95-3, Weizmann Institute of Science, The Grodetsky Center for Research of Higher Brain Functions, Israel

  7. M-O Gewaltig, A Morrison, HE Plesser (2012) Chap 18: NEST by example: an introduction to the neural simulation tool NEST. In: Le Novère N (ed) Computational Systems Neurobiology, Springer, Dordrecht, pp 533–558

  8. Kenzie-Mohr D (2000) Promoting sustainable behavior: an introduction to community-based social marketing. J Soc Issues 56(3):543–554

    Article  Google Scholar 

  9. Bergstra J, Breuleux O, Bastien F, Lamblin P, Pascanu R, Desjardins G, Turian J, Warde-Farley D, Bengio Y (2010) Theano: a CPU and GPU math expression compiler. In: Proceedings of the python for scientific computing conference (SciPy), June 2010. Oral

  10. Neuroph—Java Neural Network Framewirk (2013) Last Accessed 28 Feb 2013 Available in: http://neuroph.sourceforge.net/documentation.html

  11. Hobday M (1991) Semiconductor technology and the newly industrializing countries: the diffusion of ASICs (application specific integrated circuits). World Dev 19(4):375–397

    Article  Google Scholar 

  12. Cope B, Cheung PYK, Luk W, Witt S (2005) Have GPUs made FPGAs redundant in the field of video processing? In: Proceedings IEEE international conference on field-programmable technology, pp 111-118

  13. Omondi AR, Rajapakse JC (2006) FPGA implementation of neural network. Springer, Berlin

    Book  Google Scholar 

  14. Dias FM, Antunes A, Mota AM (2004) Artificial neural networks: a review of commercial hardware. Eng Appl Artif Intell 17(8):945–952

    Article  Google Scholar 

  15. Zhang M (2010) Artificial higher order neural networks for computer science and engineering: trends for emerging applications -Chapter 12: fifty years of electronic hardware implementations of first and higher order neural networks. IGI Global

  16. Schürmann F, Hohmann SG, Meier K, Schemmel J (2003) Interfacing binary networks to multi-valued signals. In: Supplementary proceedings of the joint international. Conference ICANN/ICONIP, pp 430–433

  17. Cox CE, Blanz E (1992) GangLion—a fast field-programmable gate array implementation of a connectionist classifier. IEEE J Solid State Circuits 28(3):288–299

    Article  Google Scholar 

  18. Alves N (2006) Investigação por inquérito, Final report, University of Azores

  19. Nemes S, Jonasson JM, Genell A, Steineck G (2009) Bias in odds ratios by logistic regression modelling and sample size. BMC Med Res Methodol 9:56 BioMedCentral

    Google Scholar 

  20. Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR (1996) A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol 49(12):1373–1379. PMID 8970487

    Google Scholar 

  21. Hasmer D, Lemeshow S (1989) Applied logistic regression. Wiley, London

    Google Scholar 

Download references

Acknowledgments

The authors would like to acknowledge the Portuguese Foundation for Science and Technology for their support for this work through project PEst-OE/EEI/LA0009/2011.

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Authors and Affiliations

  1. Madeira Interactive Technologies Institute and Competence Center of Exact Science and Engineering, University of Madeira, 9000-390, Funchal, Portugal

    Darío Baptista, Sandy Abreu, Filipe Freitas, Rita Vasconcelos & Fernando Morgado-Dias

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  1. Darío Baptista
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  2. Sandy Abreu
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  3. Filipe Freitas
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  4. Rita Vasconcelos
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  5. Fernando Morgado-Dias
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Correspondence to Darío Baptista.

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Baptista, D., Abreu, S., Freitas, F. et al. A survey of software and hardware use in artificial neural networks. Neural Comput & Applic 23, 591–599 (2013). https://doi.org/10.1007/s00521-013-1406-y

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  • DOI: https://doi.org/10.1007/s00521-013-1406-y

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