Training deep neural networks for binary communication with the Whetstone method | Nature Machine Intelligence
> Here, we describe a new approach to training SNNs, where the ANN training is to not only learn the task, but to produce a SNN in the process. Specifically, if the training procedure can include the eventual objective of low-precision communication between nodes, the training process of a SNN can be nearly as effective as a comparable ANN. This method, which we term Whetstone (Fig. 1) inspired by the tool to sharpen a dull knife, is intentionally agnostic to both the type of ANN being trained and the targeted neuromorphic hardware. Rather, the intent is to provide a straightforward interface for machine learning researchers to leverage the powerful capabilities of low-power neu-romorphic hardware on a wide range of deep learning applications Whetstone can train neural nets through Keras to be "spiking" without an expansion of the network or an expensive temporal code
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