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.
CITATION STYLE
Cococcioni, M., Rossi, F., Ruffaldi, E., & Saponara, S. (2020). A Fast Approximation of the Hyperbolic Tangent When Using Posit Numbers and Its Application to Deep Neural Networks. In Lecture Notes in Electrical Engineering (Vol. 627, pp. 213–221). Springer. https://doi.org/10.1007/978-3-030-37277-4_25
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