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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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Table of 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>