Voltage-to-Voltage Sigmoid Neuron Activation Function Design for Artificial Neural Networks
Résumé
An Artificial Neural Network (ANN) involves a complex network of interconnected nodes called artificial neurons (AN); the AN sums N weighted inputs and passes the result through a non-linear activation function (AF). In this work, a modified version of the sigmoid activation function is proposed. In order to obtain a voltage-to-voltage (V-V) transfer function required by our specific ANN, the proposed solution uses a pseudo-differential pair configuration at the input as voltage to current converter. The proposed circuit is designed in a 180nm CMOS technology of TSMC and is simulated in Cadence-Virtuoso for the proper transistor sizing in order to obtain the desired steepness of the sigmoid function. The simulations results show an overall a minimum error of 1.09 % compared to the mathematical function and power consumption of 6.77µW. Comparison with previous works and the actual mathematical function prove very favorable.
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