An experimental evaluation of extreme learning machines on several hardware devices

  • Liang Li
  • Guoren WangEmail author
  • Gang Wu
  • Qi Zhang
Extreme Learning Machine and Deep Learning Networks


As an important learning algorithm, extreme learning machine (ELM) is known for its excellent learning speed. With the expansion of ELM’s applications in the field of classification and regression, the need for its real-time performance is increasing. Although the use of hardware acceleration is an obvious solution, how to select the appropriate acceleration hardware for ELM-based applications is a topic worthy of further discussion. For this purpose, we designed and evaluated the optimized ELM algorithms on three kinds of state-of-the-art acceleration hardware, i.e., multi-core CPU, Graphics Processing Unit (GPU), and Field-Programmable Gate Array (FPGA) which are all suitable for matrix multiplication optimization. The experimental results showed that the speedup ratio of these optimized algorithms on acceleration hardware achieved 10–800. Therefore, we suggest that (1) use GPU to accelerate ELM algorithms for large dataset, and (2) use FPGA for small dataset because of its lower power, especially for some embedded applications. We also opened our source code.


Extreme learning machine Hardware Multi-core GPU FPGA 



Gang Wu is supported by the NSFC (Grant No.61872072) and the State Key Laboratory of Computer Software New Technology Open Project Fund (Grant No.KFKT2018B05). Guoren Wang is the corresponding author of this paper. Guoren Wang is supported by the NSFC (Grant No. U1401256, 61732003, 61332006 and 61729201).

Compliance with ethical standards

Conflict of interest

The authors declared that they have no conflict of interest to this work.


  1. 1.
    Alia-Martinez M, Antoñanzas J, Antonanzas-Torres F, Pernía-Espinoza A, Urraca R (2015) A straightforward implementation of a gpu-accelerated ELM in R with NVIDIA graphic cards. In: International conference on hybrid artificial intelligence systems. Springer, Berlin, pp 656–667Google Scholar
  2. 2.
    Baldi P, Sadowski P, Whiteson D (2014) Searching for exotic particles in high-energy physics with deep learning. Nat Commun 5:4308CrossRefGoogle Scholar
  3. 3.
    Deng L, Yu D, et al (2014) Deep learning: methods and applications. Found Trends® Signal Process 7(3–4):197–387Google Scholar
  4. 4.
    Ding L, Xin J, Wang G (2016) An efficient query processing optimization based on ELM in the cloud. Neural Comput Appl 27(1):35–44. CrossRefGoogle Scholar
  5. 5.
    Frances-Villora JV, Rosado-Muñoz A, Martínez-Villena JM, Bataller-Mompean M, Guerrero JF, Wegrzyn M (2016) Hardware implementation of real-time extreme learning machine in FPGA: analysis of precision, resource occupation and performance. Comput Electr Eng 51:139–156CrossRefGoogle Scholar
  6. 6.
    Hagan MT, Demuth HB, Beale MH, De Jesús O (1996) Neural network design, vol 20. Pws Pub, BostonGoogle Scholar
  7. 7.
    He Q, Du C, Wang Q, Zhuang F, Shi Z (2011) A parallel incremental extreme SVM classifier. Neurocomputing 74(16):2532–2540CrossRefGoogle Scholar
  8. 8.
    He Q, Shang T, Zhuang F, Shi Z (2013) Parallel extreme learning machine for regression based on mapreduce. Neurocomputing 102:52–58CrossRefGoogle Scholar
  9. 9.
    Huang GB, Chen L (2007) Convex incremental extreme learning machine. Neurocomputing 70(16–18):3056–3062CrossRefGoogle Scholar
  10. 10.
    Huang GB, Chen L (2008) Enhanced random search based incremental extreme learning machine. Neurocomputing 71(16–18):3460–3468CrossRefGoogle Scholar
  11. 11.
    Huang GB, Chen L, Siew CK et al (2006) Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw 17(4):879–892CrossRefGoogle Scholar
  12. 12.
    Huang GB, Ding X, Zhou H (2010) Optimization method based extreme learning machine for classification. Neurocomputing 74(1–3):155–163CrossRefGoogle Scholar
  13. 13.
    Huang GB, Liang NY, Rong HJ, Saratchandran P, Sundararajan N (2005) On-line sequential extreme learning machine. Comput Intell 2005:232–237Google Scholar
  14. 14.
    Huang GB, Wang DH, Lan Y (2011) Extreme learning machines: a survey. Int J Mach Learn Cybern 2(2):107–122CrossRefGoogle Scholar
  15. 15.
    Huang GB, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B (Cybernetics) 42(2):513–529CrossRefGoogle Scholar
  16. 16.
    Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501CrossRefGoogle Scholar
  17. 17.
    Jeowicz T, Gajdo P, Uher V, Snáel V (2015) Classification with extreme learning machine on GPU. In: 2015 international conference on intelligent networking and collaborative systems (INCOS), pp 116–122. IEEEGoogle Scholar
  18. 18.
    Li H, Wu G (2014) Map matching for taxi GPS data with extreme learning machine. In: Advanced data mining and applications—10th international conference, ADMA 2014, Guilin, China, December 19–21, 2014. Proceedings, pp 447–460.
  19. 19.
    Li J, Wang B, Wang G, Zhang Y (2016) Probabilistic threshold query optimization based on threshold classification using ELM for uncertain data. Neurocomputing 174:211–219. CrossRefGoogle Scholar
  20. 20.
    Liang NY, Huang GB, Saratchandran P, Sundararajan N (2006) A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans Neural Netw 17(6):1411–1423CrossRefGoogle Scholar
  21. 21.
    Liaw A, Wiener M et al (2002) Classification and regression by randomforest. R News 2(3):18–22Google Scholar
  22. 22.
    Ma Y, Yuan Y, Wang G, Bi X, Qin H (2018) Trust-aware personalized route query using extreme learning machine in location-based social networks. Cognit Comput 10(6):965–979. CrossRefGoogle Scholar
  23. 23.
    Magsi H, Sodhro AH, Chachar FA, Abro SAK, Sodhro GH, Pirbhulal S (2018) Evolution of 5g in internet of medical things. In: 2018 international conference on computing, mathematics and engineering technologies (iCoMET), pp 1–7. IEEEGoogle Scholar
  24. 24.
    Rong HJ, Huang GB, Sundararajan N, Saratchandran P (2009) Online sequential fuzzy extreme learning machine for function approximation and classification problems. IEEE Trans Syst Man Cybern Part B (Cybernetics) 39(4):1067–1072CrossRefGoogle Scholar
  25. 25.
    Safaei A, Wu QJ, Yang Y, Akılan T (2017) System-on-a-chip (soc)-based hardware acceleration for extreme learning machine. In: 2017 24th IEEE international conference on electronics, circuits and systems (ICECS), pp 470–473. IEEEGoogle Scholar
  26. 26.
    Schalkoff RJ (1997) Artificial neural networks, vol 1. McGraw-Hill, New YorkzbMATHGoogle Scholar
  27. 27.
    Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117CrossRefGoogle Scholar
  28. 28.
    Sodhro AH, Luo Z, Sangaiah AK, Baik SW (2019) Mobile edge computing based QOS optimization in medical healthcare applications. Int J Inf Manag 45:308–318CrossRefGoogle Scholar
  29. 29.
    Sodhro AH, Malokani AS, Sodhro GH, Muzammal M, Zongwei L (2019) An adaptive QOS computation for medical data processing in intelligent healthcare applications. Neural Comput Appl, pp 1–12Google Scholar
  30. 30.
    Sodhro AH, Pirbhulal S, de Albuquerque VHC (2019) Artificial intelligence-driven mechanism for edge computing-based industrial applications. IEEE Trans Ind Inf 15(7):4235–4243. CrossRefGoogle Scholar
  31. 31.
    Sodhro AH, Pirbhulal S, Qaraqe M, Lohano S, Sodhro GH, Junejo NUR, Luo Z (2018) Power control algorithms for media transmission in remote healthcare systems. IEEE Access 6:42384–42393CrossRefGoogle Scholar
  32. 32.
    Sodhro AH, Pirbhulal S, Sodhro GH, Gurtov A, Muzammal M, Luo Z (2018) A joint transmission power control and duty-cycle approach for smart healthcare system. IEEE Sens J 19(19):8479–8486CrossRefGoogle Scholar
  33. 33.
    Sodhro AH, Shaikh FK, Pirbhulal S, Lodro MM, Shah MA (2017) Medical-QoS based telemedicine service selection using analytic hierarchy process. In: Handbook of large-scale distributed computing in smart healthcare. Springer, Berlin, pp 589–609Google Scholar
  34. 34.
    Sun Y, Yuan Y, Wang G (2011) An OS-ELM based distributed ensemble classification framework in p2p networks. Neurocomputing 74(16):2438–2443CrossRefGoogle Scholar
  35. 35.
    Suykens JA, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300CrossRefGoogle Scholar
  36. 36.
    Van Heeswijk M, Miche Y, Oja E, Lendasse A (2011) Gpu-accelerated and parallelized elm ensembles for large-scale regression. Neurocomputing 74(16):2430–2437CrossRefGoogle Scholar
  37. 37.
    Wang B, Wang G, Li J, Wang B (2012) Update strategy based on region classification using ELM for mobile object index. Soft Comput 16(9):1607–1615CrossRefGoogle Scholar
  38. 38.
    Wang G, Zhao Y, Wang D (2008) A protein secondary structure prediction framework based on the extreme learning machine. Neurocomputing 72(1–3):262–268CrossRefGoogle Scholar
  39. 39.
    Woods L, Teubner J, Alonso G (2011) Real-time pattern matching with FPGAD. In: 2011 IEEE 27th international conference on data engineering (ICDE), pp 1292–1295. IEEEGoogle Scholar
  40. 40.
    Yeam TC, Ismail N, Mashiko K, Matsuzaki T (2017) FPGA implementation of extreme learning machine system for classification. In: Region 10 conference, TENCON 2017-2017 IEEE, pp 1868–1873. IEEEGoogle Scholar
  41. 41.
    Zhang R, Huang GB, Sundararajan N, Saratchandran P (2007) Multicategory classification using an extreme learning machine for microarray gene expression cancer diagnosis. IEEE/ACM Trans Comput Biol Bioinform (TCBB) 4(3):485–495CrossRefGoogle Scholar
  42. 42.
    Zhang Z, Zhao X, Wang G, Bi X (2018) A new point-of-interest classification model with an extreme learning machine. Cognit Comput 10(6):951–964. CrossRefGoogle Scholar
  43. 43.
    Xg Zhao, Wang G, Bi X, Gong P, Zhao Y (2011) XML document classification based on ELM. Neurocomputing 74(16):2444–2451CrossRefGoogle Scholar
  44. 44.
    Zhu QY, Qin AK, Suganthan PN, Huang GB (2005) Evolutionary extreme learning machine. Pattern Recognit 38(10):1759–1763CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Computer Science and EngineeringNortheastern UniversityShenyangChina
  2. 2.Computer Science and TechnologyBeijing Institute of TechnologyBeijingChina
  3. 3.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina

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