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M. Cococcioni, F. Rossi, E. Ruffaldi, and S. Saponara. "A fast approximation of the hyperbolic tangent when using posit numbers and its application to deep neural networks", (ApplePies’19), 2019.

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Deep Neural Networks (DNNs) are being used in more and more fields. Among the others, automotive is a field where deep neural networks are being exploited the most. An important aspect to be considered is the real-time constraint that this kind of applications put on neural network architectures. This poses the need for fast and hardware-friendly information representation. The recently proposed Posit format has been proved to be extremely efficient as a low-bit replacement of traditional floats. Its format has already allowed to construct a fast approximation of the sigmoid function, an activation function frequently used in DNNs. In this paper we present a fast approximation of another activation function widely used in DNNs: the hyperbolic tangent. In the experiment, we show how the approximated hyperbolic function outperforms the approximated sigmoid counterpart. The implication is clear: the posit format shows itself to be again DNN friendly, with important outcomes.

Keywords: {Deep Neural Networks (DNNs), Posit, Activation functions}