Hardware Implementation of Tanh Exponential Activation Function using FPGA
Safa Bouguezzi, Hassene Faiedh, Chokri Souani
Abstract
The most active research area for Field Programmable Gate Arrays is the Convolution Neural Network (CNN), and the gist of any CNN is an activation function. Therefore, various non-linear activation functions are required for deeper CNN. In this paper, we aim to implement the Tanh Exponential (TanhExp) activation function on Artix-7 and Zynq-7000. To this end, we will use the piecewise linear approximation and the second-order polynomial approximation while using the IEEE754 2008 floating-point representation. We present an investigation of the required hardware resources. We also evaluate the efficiency of each method of approximation and its derivative.
Topics & Concepts
Activation functionHyperbolic functionField-programmable gate arrayConvolution (computer science)Exponential functionComputer sciencePiecewisePolynomialFunction (biology)Convolutional neural networkAlgorithmFunction approximationPiecewise linear functionArtificial neural networkParallel computingComputer hardwareMathematicsArtificial intelligenceMathematical analysisEvolutionary biologyBiologyNeural Networks and ApplicationsAdvanced Neural Network ApplicationsMachine Learning and ELM