PowerGenie: Analytically-Guided Evolutionary Discovery of Superior Reconfigurable Power Converters
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866917362012258304 |
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| author | Gao, Jian Zou, Yiwei Pradhan, Abhishek Huang, Wenhao Su, Yumin Yang, Kaiyuan Zhang, Xuan |
| author_facet | Gao, Jian Zou, Yiwei Pradhan, Abhishek Huang, Wenhao Su, Yumin Yang, Kaiyuan Zhang, Xuan |
| contents | Discovering superior circuit topologies requires navigating an exponentially large design space-a challenge traditionally reserved for human experts. Existing AI methods either select from predefined templates or generate novel topologies at a limited scale without rigorous verification, leaving large-scale performance-driven discovery underexplored. We present PowerGenie, a framework for automated discovery of higher-performance reconfigurable power converters at scale. PowerGenie introduces: (1) an automated analytical framework that determines converter functionality and theoretical performance limits without component sizing or SPICE simulation, and (2) an evolutionary finetuning method that co-evolves a generative model with its training distribution through fitness selection and uniqueness verification. Unlike existing methods that suffer from mode collapse and overfitting, our approach achieves higher syntax validity, function validity, novelty rate, and figure-of-merit (FoM). PowerGenie discovers a novel 8-mode reconfigurable converter with 23% higher FoM than the best training topology. SPICE simulations confirm average absolute efficiency gains of 10% across 8 modes and up to 17% at a single mode. Code will be released upon publication. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_21984 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PowerGenie: Analytically-Guided Evolutionary Discovery of Superior Reconfigurable Power Converters Gao, Jian Zou, Yiwei Pradhan, Abhishek Huang, Wenhao Su, Yumin Yang, Kaiyuan Zhang, Xuan Machine Learning Hardware Architecture Discovering superior circuit topologies requires navigating an exponentially large design space-a challenge traditionally reserved for human experts. Existing AI methods either select from predefined templates or generate novel topologies at a limited scale without rigorous verification, leaving large-scale performance-driven discovery underexplored. We present PowerGenie, a framework for automated discovery of higher-performance reconfigurable power converters at scale. PowerGenie introduces: (1) an automated analytical framework that determines converter functionality and theoretical performance limits without component sizing or SPICE simulation, and (2) an evolutionary finetuning method that co-evolves a generative model with its training distribution through fitness selection and uniqueness verification. Unlike existing methods that suffer from mode collapse and overfitting, our approach achieves higher syntax validity, function validity, novelty rate, and figure-of-merit (FoM). PowerGenie discovers a novel 8-mode reconfigurable converter with 23% higher FoM than the best training topology. SPICE simulations confirm average absolute efficiency gains of 10% across 8 modes and up to 17% at a single mode. Code will be released upon publication. |
| title | PowerGenie: Analytically-Guided Evolutionary Discovery of Superior Reconfigurable Power Converters |
| topic | Machine Learning Hardware Architecture |
| url | https://arxiv.org/abs/2601.21984 |