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| Format: | Recurso digital |
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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.15208403 |
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| _version_ | 1866901126065946624 |
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| author | hasegawa, yoshihiro |
| author_facet | hasegawa, yoshihiro |
| contents | <p>We propose a hardware-theoretic framework for implementing p-adic deep learning models using Feynman categories and fiber bundle structures. By interpreting neural network architectures as morphisms in a Feynman category and assigning p-adic representation spaces as fibers, we derive a design methodology for ASIC/FPGA accelerators that exploit local coordinate charts and transition functions for efficient computation.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15208403 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Hardware Theory of Feynman Category Fiber Bundles for p-Adic Deep Learning hasegawa, yoshihiro <p>We propose a hardware-theoretic framework for implementing p-adic deep learning models using Feynman categories and fiber bundle structures. By interpreting neural network architectures as morphisms in a Feynman category and assigning p-adic representation spaces as fibers, we derive a design methodology for ASIC/FPGA accelerators that exploit local coordinate charts and transition functions for efficient computation.</p> |
| title | Hardware Theory of Feynman Category Fiber Bundles for p-Adic Deep Learning |
| url | https://doi.org/10.5281/zenodo.15208403 |