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.

A Fast Approximation of the Hyperbolic Tangent When Using Posit Numbers and Its Application to Deep Neural Networks

Cococcioni M.
Co-primo
;
Rossi F.
Co-primo
;
Ruffaldi E.
Co-primo
;
Saponara S.
Co-primo
2020-01-01

Abstract

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.
2020
Cococcioni, M.; Rossi, F.; Ruffaldi, E.; Saponara, S.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1040240
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