Digital Hardware Implementations of Spiking Neural Networks With Selective Input Sparsity for Edge Inferences in Controlled Image Acquisition Environments
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DOI: 10.1109/tcsii.2023.3260704
Abstract: Digital hardware implementations of Spiking Neural Networks designed using Selective Input Sparsity (SIS) are proposed for edge inference applications in image classification where the image acquisition environment is controlled. These sparsely connected networks are well-suited… read more here.
Keywords: neural networks; spiking neural; digital hardware; hardware implementations ... See more keywords