LARGE NEURAL NETWORK: OBJECT MODELING AND PARALLEL SIMULATION

Article Properties
  • Language
    English
  • DOI (url)
    10.1142/s0218213001000568
  • Publication Date
    2001/09/01
  • Journal
    International Journal on Artificial Intelligence Tools
  • Indian UGC (Journal)
  • Refrences
    4
  • C. J. GARCÍA-ORELLANA Departamento de Electrónica e Ing. Electromecánica, Universidad de Extremadura – 06071 Badajoz, Spain
  • F. J. LÓPEZ-ALIGUÉ Departamento de Electrónica e Ing. Electromecánica, Universidad de Extremadura – 06071 Badajoz, Spain
  • H. M. GONZÁLEZ-VELASCO Departamento de Electrónica e Ing. Electromecánica, Universidad de Extremadura – 06071 Badajoz, Spain
  • M. MACÍAS-MACÍAS Departamento de Electrónica e Ing. Electromecánica, Universidad de Extremadura – 06071 Badajoz, Spain
  • M. I. ACEVEDO-SOTOCA Departamento de Electrónica e Ing. Electromecánica, Universidad de Extremadura – 06071 Badajoz, Spain
Abstract
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Cite
GARCÍA-ORELLANA, C. J., et al. “LARGE NEURAL NETWORK: OBJECT MODELING AND PARALLEL SIMULATION”. International Journal on Artificial Intelligence Tools, vol. 10, no. 03, 2001, pp. 373-85, https://doi.org/10.1142/s0218213001000568.
GARCÍA-ORELLANA, C. J., LÓPEZ-ALIGUÉ, F. J., GONZÁLEZ-VELASCO, H. M., MACÍAS-MACÍAS, M., & ACEVEDO-SOTOCA, M. I. (2001). LARGE NEURAL NETWORK: OBJECT MODELING AND PARALLEL SIMULATION. International Journal on Artificial Intelligence Tools, 10(03), 373-385. https://doi.org/10.1142/s0218213001000568
GARCÍA-ORELLANA CJ, LÓPEZ-ALIGUÉ FJ, GONZÁLEZ-VELASCO HM, MACÍAS-MACÍAS M, ACEVEDO-SOTOCA MI. LARGE NEURAL NETWORK: OBJECT MODELING AND PARALLEL SIMULATION. International Journal on Artificial Intelligence Tools. 2001;10(03):373-85.
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Description

How can we efficiently simulate large neural networks? This paper proposes an object-oriented model for simulating large neural networks using the OMT technique. The server, called NeuSim-NNLIB, is a "beowulf" cluster getting up to 18 MCPS with a cluster of 6 Pentium processors. The modeling has been implemented on a client-server parallel simulator. The simulator's performance and optimal processor number are also estimated. By providing a parallel simulation approach, the study offers valuable insights for researchers and developers working with complex neural network models. The architecture and performance analysis can help guide the design of future simulation platforms.

Refrences
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