Voltage-to-Voltage Sigmoid Neuron Activation Function Design for Artificial Neural Networks

Tatiana Moposita, Lionel Trojman, Felice Crupi, Marco Lanuzza, Andrei Vladimirescu

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

3 Citas (Scopus)

Resumen

An Artificial Neural Network (ANN) involves a complex network of interconnected nodes called artificial neurons (AN); the AN sums N weighted inputs and send thought the result to a non-linear activation function (AF). In this work, a modified version of the sigmoid activation function is proposed. To obtain a voltage-to-voltage (V - V) transfer function required by an specific ANN. The proposed solution uses a pseudo-differential pair configuration at the input as voltage to current converter. The proposed circuit is designed using a commercial PDK in 180nm (TSMC) and is simulated in Virtuoso (Cadence). This specific design enable to obtain the desired steepness of the sigmoid function by means of the proper transistor sizing. Simulation results of our specific design show that we can reach an average relative error of only 1.09 % for steepness of 1 as compared to the exact mathematical function, and a power consumption of 6.77μW for steepness of 10.

Idioma originalInglés
Título de la publicación alojada2022 IEEE 13th Latin American Symposium on Circuits and Systems, LASCAS 2022
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781665420082
DOI
EstadoPublicada - 2022
Publicado de forma externa
Evento13th IEEE Latin American Symposium on Circuits and Systems, LASCAS 2022 - Santiago, Chile
Duración: 1 mar. 20224 mar. 2022

Serie de la publicación

Nombre2022 IEEE 13th Latin American Symposium on Circuits and Systems, LASCAS 2022

Conferencia

Conferencia13th IEEE Latin American Symposium on Circuits and Systems, LASCAS 2022
País/TerritorioChile
CiudadSantiago
Período1/03/224/03/22

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