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Author(s):
Daniel Auge, Etienne Mueller 
Title:
Resonate-and-Fire Neurons as Frequency Selective Input Encoders for Spiking Neural Networks 
Abstract:
More and more embedded applications demand to cope with complex data while still being energy-efficient. Neural networks provide the processing capabilities, but often cannot be utilised because of restricted power goals. Spiking neural networks have been shown to potentially solve this problem due to their hardware friendliness and energy efficiency. One remaining problem is the conversion of input data into event-based spikes in order to be processed. In this study, we examine using resonating...    »
 
Year:
2020