Toward End-to-End Analog Design Automation with ML and Data-Driven Approaches

Designing analog circuits poses significant challenges due to their knowledge-intensive nature and the diverse range of requirements. There has been limited success in achieving a fully automated framework for designing analog circuits. However, the advent of advanced machine learning algorithms is...

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Bibliographic Details
Main Authors: Maji, Supriyo, Budak, Ahmet F., Poddar, Souradip, Pan, David Z.
Format: Conference Proceeding
Language:English
Subjects:
Applied computing
Applied computing > Arts and humanities
Applied computing > Arts and humanities > Architecture (buildings)
Applied computing > Arts and humanities > Architecture (buildings) > Computer-aided design
Applied computing > Physical sciences and engineering
Applied computing > Physical sciences and engineering > Engineering
Asia
Automation
Design automation
Hardware
Hardware > Electronic design automation
Hardware > Electronic design automation > Physical design (EDA)
Layout
Machine learning algorithms
Transforms
Visualization
Online Access:Request full text
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Summary:Designing analog circuits poses significant challenges due to their knowledge-intensive nature and the diverse range of requirements. There has been limited success in achieving a fully automated framework for designing analog circuits. However, the advent of advanced machine learning algorithms is invigorating design automation efforts by enabling tools to replicate the techniques employed by experienced designers. In this paper, we aim to provide an overview of the recent progress in ML-driven analog circuit sizing and layout automation tool developments. In advanced technology nodes, layout effects must be considered during circuit sizing to avoid costly rerun of the flow. We will discuss the latest research in layout-aware sizing. In the end-to-end analog design automation flow, topology selection plays an important role, as the final performance depends on the choice of topology. We will discuss recent developments in ML-driven topology selection before delving into our vision of an end-to-end data-driven framework that leverages ML techniques to facilitate the selection of optimal topology from a library of topologies.
ISSN:2153-697X
DOI:10.1109/ASP-DAC58780.2024.10473840