Use of Virtual Forward Propagation Network Model to Translate Analog Components

Muhammad Sana Ullah; William Brickner; Emadelden Fouad

doi:doi:10.11648/j.cssp.20200901.13

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Use of Virtual Forward Propagation Network Model to Translate Analog Components

Muhammad Sana Ullah, William Brickner, Emadelden Fouad

Published in Science Journal of Circuits, Systems and Signal Processing (Volume 9, Issue 1)

Received: 1 June 2020 Accepted: 17 June 2020 Published: 17 July 2020

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Abstract

Neural computing is an emerging research topic today due to its massive increase in demand and applications for machine learning. In this virtual simulation research work, using a free software, a program has been trained a neural network model and translate its functionality into the hardware. In the context of analog neural network, this research seeks to verify a shift sigmoid function that can approximate the transfer function of CMOS inverter. By showing this approximation accurately and reducing the number of components, it would help to implement the neural network based integrated chips. A conciliation is selected for the distance matric of the proposed function. This distance metric between the given CMOS transfer function and the shifted sigmoid function is minimized using the gradient descent. However, this approximate transfer function of CMOS inverter is chosen to verify in a three-layer perceptron networks. The network topology randomly generates weights to provide a diverse set of truth tables. We report two networks whose weights are chosen randomly using a back propagation algorithm due to volatile nature of the network topology and the activation function. The results of this research conclude that the transfer function of CMOS inverter is able to approximate the CMOS transfer function adequately for the purposes of these perceptron networks.

Published in	Science Journal of Circuits, Systems and Signal Processing (Volume 9, Issue 1)
DOI	10.11648/j.cssp.20200901.13
Page(s)	24-30
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Analog Components, Artificial Neural Network, Machine Learning, Universal Gates, Virtual Network

References

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APA Style

Muhammad Sana Ullah, William Brickner, Emadelden Fouad. (2020). Use of Virtual Forward Propagation Network Model to Translate Analog Components. Science Journal of Circuits, Systems and Signal Processing, 9(1), 24-30. https://doi.org/10.11648/j.cssp.20200901.13

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ACS Style

Muhammad Sana Ullah; William Brickner; Emadelden Fouad. Use of Virtual Forward Propagation Network Model to Translate Analog Components. Sci. J. Circuits Syst. Signal Process. 2020, 9(1), 24-30. doi: 10.11648/j.cssp.20200901.13

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AMA Style

Muhammad Sana Ullah, William Brickner, Emadelden Fouad. Use of Virtual Forward Propagation Network Model to Translate Analog Components. Sci J Circuits Syst Signal Process. 2020;9(1):24-30. doi: 10.11648/j.cssp.20200901.13

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@article{10.11648/j.cssp.20200901.13,
  author = {Muhammad Sana Ullah and William Brickner and Emadelden Fouad},
  title = {Use of Virtual Forward Propagation Network Model to Translate Analog Components},
  journal = {Science Journal of Circuits, Systems and Signal Processing},
  volume = {9},
  number = {1},
  pages = {24-30},
  doi = {10.11648/j.cssp.20200901.13},
  url = {https://doi.org/10.11648/j.cssp.20200901.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cssp.20200901.13},
  abstract = {Neural computing is an emerging research topic today due to its massive increase in demand and applications for machine learning. In this virtual simulation research work, using a free software, a program has been trained a neural network model and translate its functionality into the hardware. In the context of analog neural network, this research seeks to verify a shift sigmoid function that can approximate the transfer function of CMOS inverter. By showing this approximation accurately and reducing the number of components, it would help to implement the neural network based integrated chips. A conciliation is selected for the distance matric of the proposed function. This distance metric between the given CMOS transfer function and the shifted sigmoid function is minimized using the gradient descent. However, this approximate transfer function of CMOS inverter is chosen to verify in a three-layer perceptron networks. The network topology randomly generates weights to provide a diverse set of truth tables. We report two networks whose weights are chosen randomly using a back propagation algorithm due to volatile nature of the network topology and the activation function. The results of this research conclude that the transfer function of CMOS inverter is able to approximate the CMOS transfer function adequately for the purposes of these perceptron networks.},
 year = {2020}
}

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TY  - JOUR
T1  - Use of Virtual Forward Propagation Network Model to Translate Analog Components
AU  - Muhammad Sana Ullah
AU  - William Brickner
AU  - Emadelden Fouad
Y1  - 2020/07/17
PY  - 2020
N1  - https://doi.org/10.11648/j.cssp.20200901.13
DO  - 10.11648/j.cssp.20200901.13
T2  - Science Journal of Circuits, Systems and Signal Processing
JF  - Science Journal of Circuits, Systems and Signal Processing
JO  - Science Journal of Circuits, Systems and Signal Processing
SP  - 24
EP  - 30
PB  - Science Publishing Group
SN  - 2326-9073
UR  - https://doi.org/10.11648/j.cssp.20200901.13
AB  - Neural computing is an emerging research topic today due to its massive increase in demand and applications for machine learning. In this virtual simulation research work, using a free software, a program has been trained a neural network model and translate its functionality into the hardware. In the context of analog neural network, this research seeks to verify a shift sigmoid function that can approximate the transfer function of CMOS inverter. By showing this approximation accurately and reducing the number of components, it would help to implement the neural network based integrated chips. A conciliation is selected for the distance matric of the proposed function. This distance metric between the given CMOS transfer function and the shifted sigmoid function is minimized using the gradient descent. However, this approximate transfer function of CMOS inverter is chosen to verify in a three-layer perceptron networks. The network topology randomly generates weights to provide a diverse set of truth tables. We report two networks whose weights are chosen randomly using a back propagation algorithm due to volatile nature of the network topology and the activation function. The results of this research conclude that the transfer function of CMOS inverter is able to approximate the CMOS transfer function adequately for the purposes of these perceptron networks.
VL  - 9
IS  - 1
ER  -

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Author Information

Muhammad Sana Ullah

Department of Electrical and Computer Engineering, Florida Polytechnic University, Lakeland, USA
William Brickner

Department of Electrical and Computer Engineering, Florida Polytechnic University, Lakeland, USA
Emadelden Fouad

Department of Natural Sciences, Florida Polytechnic University, Lakeland, USA

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Plain Text BibTeX RIS

APA Style

Muhammad Sana Ullah, William Brickner, Emadelden Fouad. (2020). Use of Virtual Forward Propagation Network Model to Translate Analog Components. Science Journal of Circuits, Systems and Signal Processing, 9(1), 24-30. https://doi.org/10.11648/j.cssp.20200901.13

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ACS Style

Muhammad Sana Ullah; William Brickner; Emadelden Fouad. Use of Virtual Forward Propagation Network Model to Translate Analog Components. Sci. J. Circuits Syst. Signal Process. 2020, 9(1), 24-30. doi: 10.11648/j.cssp.20200901.13

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AMA Style

Muhammad Sana Ullah, William Brickner, Emadelden Fouad. Use of Virtual Forward Propagation Network Model to Translate Analog Components. Sci J Circuits Syst Signal Process. 2020;9(1):24-30. doi: 10.11648/j.cssp.20200901.13

Copy | Download

@article{10.11648/j.cssp.20200901.13,
  author = {Muhammad Sana Ullah and William Brickner and Emadelden Fouad},
  title = {Use of Virtual Forward Propagation Network Model to Translate Analog Components},
  journal = {Science Journal of Circuits, Systems and Signal Processing},
  volume = {9},
  number = {1},
  pages = {24-30},
  doi = {10.11648/j.cssp.20200901.13},
  url = {https://doi.org/10.11648/j.cssp.20200901.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cssp.20200901.13},
  abstract = {Neural computing is an emerging research topic today due to its massive increase in demand and applications for machine learning. In this virtual simulation research work, using a free software, a program has been trained a neural network model and translate its functionality into the hardware. In the context of analog neural network, this research seeks to verify a shift sigmoid function that can approximate the transfer function of CMOS inverter. By showing this approximation accurately and reducing the number of components, it would help to implement the neural network based integrated chips. A conciliation is selected for the distance matric of the proposed function. This distance metric between the given CMOS transfer function and the shifted sigmoid function is minimized using the gradient descent. However, this approximate transfer function of CMOS inverter is chosen to verify in a three-layer perceptron networks. The network topology randomly generates weights to provide a diverse set of truth tables. We report two networks whose weights are chosen randomly using a back propagation algorithm due to volatile nature of the network topology and the activation function. The results of this research conclude that the transfer function of CMOS inverter is able to approximate the CMOS transfer function adequately for the purposes of these perceptron networks.},
 year = {2020}
}

Copy | Download

TY  - JOUR
T1  - Use of Virtual Forward Propagation Network Model to Translate Analog Components
AU  - Muhammad Sana Ullah
AU  - William Brickner
AU  - Emadelden Fouad
Y1  - 2020/07/17
PY  - 2020
N1  - https://doi.org/10.11648/j.cssp.20200901.13
DO  - 10.11648/j.cssp.20200901.13
T2  - Science Journal of Circuits, Systems and Signal Processing
JF  - Science Journal of Circuits, Systems and Signal Processing
JO  - Science Journal of Circuits, Systems and Signal Processing
SP  - 24
EP  - 30
PB  - Science Publishing Group
SN  - 2326-9073
UR  - https://doi.org/10.11648/j.cssp.20200901.13
AB  - Neural computing is an emerging research topic today due to its massive increase in demand and applications for machine learning. In this virtual simulation research work, using a free software, a program has been trained a neural network model and translate its functionality into the hardware. In the context of analog neural network, this research seeks to verify a shift sigmoid function that can approximate the transfer function of CMOS inverter. By showing this approximation accurately and reducing the number of components, it would help to implement the neural network based integrated chips. A conciliation is selected for the distance matric of the proposed function. This distance metric between the given CMOS transfer function and the shifted sigmoid function is minimized using the gradient descent. However, this approximate transfer function of CMOS inverter is chosen to verify in a three-layer perceptron networks. The network topology randomly generates weights to provide a diverse set of truth tables. We report two networks whose weights are chosen randomly using a back propagation algorithm due to volatile nature of the network topology and the activation function. The results of this research conclude that the transfer function of CMOS inverter is able to approximate the CMOS transfer function adequately for the purposes of these perceptron networks.
VL  - 9
IS  - 1
ER  -

Copy | Download