2010 · 16 citations · 11 references
EngineeringComputer EngineeringComputer ScienceEmbedded SystemsField-programmable Gate ArraysHardware Description LanguagesEnergy-efficient Embedded SystemsField-programmable Gate Array ArchitectureFpga ArchitectureLow-power Digital Circuit DesignLow-power Fpga DesignPower ManagementHardware Description Language ImplementationPower ConsumptionPower Consumption ModelingPower Consumption OptimizationPower-aware Fpga ArchitecturePower-aware Hardware ArchitecturePower-efficient ArchitecturePower-efficient Design
Artificial Neural Networks (ANN) are used to perform tasks like classification, pattern recognition and function approximations in many cases to which traditional approaches are not well suited. Hardware implementations have been presented, mainly in academical works, in order to take advantage of the inherent parallelism in ANNs. In the field of embedded systems it is desirable to have faster and less power demanding designs. This work analyzes implementations of ANNs in FPGAs both in Hardware Description Language (HDL) and in software code running on different configurations of the Xilinx MicroBlaze microprocessor. Three versions of an ANN design were implemented in HDL and a software version was executed in four different configurations of the Xilinx MicroBlaze microprocessor. Results for power consumption, FPGA occupation, speed and accuracy of the outputs are presented for practical experiments performed in two FPGAs from different families of Xilinx devices: a Spartan 3E and a Virtex 5.
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Cyber Physical Systems: Design Challenges
Edward A. Lee · 2008 · 3.5K citations
Challenges for large-scale implementations of spiking neural networks on FPGAs
Liam Maguire, T.M. McGinnity, Brendan Glackin et al. · Neurocomputing · 2007 · 179 citations