Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks

13 Dec 2016Bodo RueckauerIulia-Alexandra LunguYuhuang HuMichael Pfeiffer

Deep convolutional neural networks (CNNs) have shown great potential for numerous real-world machine learning applications, but performing inference in large CNNs in real-time remains a challenge. We have previously demonstrated that traditional CNNs can be converted into deep spiking neural networks (SNNs), which exhibit similar accuracy while reducing both latency and computational load as a consequence of their data-driven, event-based style of computing... (read more)

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