AI-Powered Agile Analog Circuit Design and Optimization
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916725827567616 |
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| author | Hu, Jinhai Goh, Wang Ling Gao, Yuan |
| author_facet | Hu, Jinhai Goh, Wang Ling Gao, Yuan |
| contents | Artificial intelligence (AI) techniques are transforming analog circuit design by automating device-level tuning and enabling system-level co-optimization. This paper integrates two approaches: (1) AI-assisted transistor sizing using Multi-Objective Bayesian Optimization (MOBO) for direct circuit parameter optimization, demonstrated on a linearly tunable transconductor; and (2) AI-integrated circuit transfer function modeling for system-level optimization in a keyword spotting (KWS) application, demonstrated by optimizing an analog bandpass filter within a machine learning training loop. The combined insights highlight how AI can improve analog performance, reduce design iteration effort, and jointly optimize analog components and application-level metrics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_03750 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-Powered Agile Analog Circuit Design and Optimization Hu, Jinhai Goh, Wang Ling Gao, Yuan Hardware Architecture Artificial Intelligence Artificial intelligence (AI) techniques are transforming analog circuit design by automating device-level tuning and enabling system-level co-optimization. This paper integrates two approaches: (1) AI-assisted transistor sizing using Multi-Objective Bayesian Optimization (MOBO) for direct circuit parameter optimization, demonstrated on a linearly tunable transconductor; and (2) AI-integrated circuit transfer function modeling for system-level optimization in a keyword spotting (KWS) application, demonstrated by optimizing an analog bandpass filter within a machine learning training loop. The combined insights highlight how AI can improve analog performance, reduce design iteration effort, and jointly optimize analog components and application-level metrics. |
| title | AI-Powered Agile Analog Circuit Design and Optimization |
| topic | Hardware Architecture Artificial Intelligence |
| url | https://arxiv.org/abs/2505.03750 |