Abstract
This paper investigates the use of a network of cascaded Field Programmable Analogue Arrays (FPAAs) to implement an evolved, analogue, Spiking Neural Network (SNN) pole balance controller. The SNN hardware platform interfaces to a simulated pole balancing model for evaluation. Performance of the evolved analogue hardware controller is compared to that of a software-based SNN controller. The evolved hardware network displays an improved tolerance to changing environments compared with networks evolved solely in simulation. The paper goes on to discuss the suitability of low density FPAA devices for analogue-centric hardware neural network platforms. It concludes by outlining some possible directions which address the observed limitations of using FPAAs for ANNs.
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Rocke, P., McGinley, B., Maher, J., Morgan, F., Harkin, J. (2008). Investigating the Suitability of FPAAs for Evolved Hardware Spiking Neural Networks. In: Hornby, G.S., Sekanina, L., Haddow, P.C. (eds) Evolvable Systems: From Biology to Hardware. ICES 2008. Lecture Notes in Computer Science, vol 5216. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85857-7_11
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DOI: https://doi.org/10.1007/978-3-540-85857-7_11
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