Evolving advanced neural networks on run-time reconfigurable digital hardware platform

Authors:
László Bakó;Sándor Tihamér Brassai;László Ferenc Márton;Lajos Losonczi
Affiliations:
Sapientia - Hungarian University of Transylvania, Romania;Sapientia - Hungarian University of Transylvania, Romania;Sapientia - Hungarian University of Transylvania, Romania;Lambda Communication Ltd., Tirgu-Mures, Romania
Venue:
Proceedings of the 3rd International Workshop on Adaptive Self-Tuning Computing Systems
Year:
2013

Citing 3
Cited 0

Real-time computing without stable states: a new framework for neural computation based on perturbations

Neural Computation
FPGA Implementations of Neural Networks

FPGA Implementations of Neural Networks
FPGA Accelerated Simulation of Biologically Plausible Spiking Neural Networks

FCCM '09 Proceedings of the 2009 17th IEEE Symposium on Field Programmable Custom Computing Machines

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Abstract

This paper describes the development of a framework for prototyping evolvable hardware Spiking Neural Networks using runtime reconfigurable systems in a Xilinx FPGAs. Practical implementations are focused on the classification of acquired EEG signals that are processed using the wavelet transform. The dynamic run-time partial reconfiguration (PR) capability of the Virtex FPGA is used to interchange those units of the system -- therefore saving precious resources -- that do not run their algorithms in parallel.