FPGA implementation of spiking neural networks-an initial step towards building tangible collaborative autonomous agents

S. Bellis, Kafil Mahmood Razeeb, Chitta Saha, Kieran Delaney, C. O'Mathuna, Anthony Pounds-Cornish, Gustavo de Souza, Martin Colley, Hani Hagras, Graham Clarke, V. Callaghan, C. Argyropoulos, C. Karistianos, G. Nikiforidis

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    This work contains the results of an initial study into the FPGA implementation of a spiking neural network. This work was undertaken as a task in a project that aims to design and develop a new kind of tangible collaborative autonomous agent. The project intends to exploit/investigate methods for engineering emergent collective behaviour in large societies of actual miniature agents that can learn and evolve. Such multi-agent systems could be used to detect and collectively repair faults in a variety of applications where it is difficult for humans to gain access, such as fluidic environments found in critical components of material/industrial systems. The initial achievement of implementation of a spiking neural network on a FPGA hardware platform and results of a robotic wall following task are discussed by comparison with software driven robots and simulations.
    Original languageEnglish
    Title of host publication2004 IEEE International Conference on Field-Programmable Technology, 2004. Proceedings.
    DOIs
    Publication statusPublished (VoR) - 14 Feb 2005

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