Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)

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Shape and Margin Dynamics in Analog Computing: A Machine Learning Perspective

Authors
Abhishek Agwekar1, *, Laxmi Singh2
1Ph D. Scholar, Department of Electronics & Communication Engineering, Rabindranath Tagore University (RNTU), Bhopal, India
2Professor & Head, Department of Electronics & Communication Engineering, Rabindranath Tagore University (RNTU), Bhopal, India
*Corresponding author. Email: abhiagwekar@gmail.com
Corresponding Author
Abhishek Agwekar
Available Online 26 May 2025.
DOI
10.2991/978-94-6463-716-8_65How to use a DOI?
Keywords
Process Scalability; Margin Propagation; Machine Learning; S-AC Computing
Abstract

This study examines the design of shape-based analog computing (S-AC) circuits using a margin-propagation-based analog computing circuit. The Scaling of the circuit with digital designs S-AC designs are precision, speed, and power. For the implementation of the S-AC circuits mathematical functions are used with machine learning architectures. For circuit simulations input/output characteristics are mapped from a CMOS process. Accuracy of S-AC based neural network is robust to use when changes temperature with the parameters. The basic S-AC remains scalable process when the increase in the number of splines, increases the accuracy.This paper also focuses on the Design Margin and Shape Analysis. The design parameter S and machine learning applications both benefit from this form. The system may precisely replicate the desired functional form. Instead of using traditional design methods, S-AC design lets the user select the proto-shape based on the application’s requirements and concentrate on obtaining the appropriate functional forms.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)
Series
Advances in Intelligent Systems Research
Publication Date
26 May 2025
ISBN
978-94-6463-716-8
ISSN
1951-6851
DOI
10.2991/978-94-6463-716-8_65How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Abhishek Agwekar
AU  - Laxmi Singh
PY  - 2025
DA  - 2025/05/26
TI  - Shape and Margin Dynamics in Analog Computing: A Machine Learning Perspective
BT  - Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)
PB  - Atlantis Press
SP  - 879
EP  - 891
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-716-8_65
DO  - 10.2991/978-94-6463-716-8_65
ID  - Agwekar2025
ER  -