Exploring Shape and Margin in Analog Computing Circuits: A Machine Learning-Based Approach to Design and Performance Evaluation

Exploring Shape and Margin in Analog Computing Circuits: A Machine Learning-Based Approach to Design and Performance Evaluation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploring Shape and Margin in Analog Computing Circuits: A Machine Learning-Based Approach to Design and Performance Evaluation Abhishek Agwekar, Laxmi Singh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6140725/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract This research focuses on the design and analysis of shape-based analog computing (S-AC) circuits utilizing the margin-propagation method. It examines the fundamental characteristics of S-AC circuits, particularly their scalability in terms of precision, speed, and power efficiency when compared to digital alternatives. The development of S-AC circuits integrates machine learning (ML) architectures with mathematical functions, and their input-output characteristics are modeled using a CMOS process for circuit simulations. A key advantage of S-AC-based neural networks is their robustness against temperature variations, ensuring consistent accuracy. Additionally, as the accuracy of the fundamental S-AC process improves with multiple splines, scalability remains unaffected. This paper also highlights the significance of Design Margin and Shape Analysis, where the design parameter SSS and ML-based techniques are critical in shaping the system's ability to replicate the desired functional form. Unlike conventional design approaches, S-AC design provides flexibility by allowing users to define the proto-shape based on application-specific requirements, prioritizing the achievement of precise functional forms. Fabrication Scalability error margin predictive modelling S-AC Computing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6140725","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":435776832,"identity":"3cd2c62b-e651-4e6d-8b3f-16096d0a2557","order_by":0,"name":"Abhishek 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