Metraipolis › archive › dc_cd2bd0ec887e

SH-WRNN: Implicit Spherical Harmonics Weight Field Routing Neural Networks for Asymmetric Edge Intelligence — arXiv:2609.14614

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.14614
License (as archived): CC0-1.0
Archived: 2026-10-16T00:10:50.892Z
SHA-256 of the archived text: 279e1a60434906c68ebf0a92a185093019fcf6738dab1e7f525a74d609baeb1f
Stable URL of this page: https://metraipolis.com/documento/dc_cd2bd0ec887e

How to cite

SH-WRNN: Implicit Spherical Harmonics Weight Field Routing Neural Networks for Asymmetric Edge Intelligence — arXiv:2609.14614 — arXiv (en), https://arxiv.org/abs/2609.14614 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_cd2bd0ec887e (Metraipolis archive dc_cd2bd0ec887e, sha256 279e1a60434906c68ebf0a92a185093019fcf6738dab1e7f525a74d609baeb1f)

BibTeX

@misc{dccd2bd0ec887e,
  title = {SH-WRNN: Implicit Spherical Harmonics Weight Field Routing Neural Networks for Asymmetric Edge Intelligence — arXiv:2609.14614},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.14614},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_cd2bd0ec887e; sha256 279e1a60434906c68ebf0a92a185093019fcf6738dab1e7f525a74d609baeb1f},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.14614. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Deep learning architectures remain rigidly built upon traditional fully connected layers. While networks scale up, few challenge this foundational root. In this work, we reshape this paradigm by transforming the core synapse weight matrix from static, discrete parameters into a differentiable, continuous field governed by spherical harmonics functions. We introduce the Implicit Spherical Harmonics Weight Field Routing Neural Network (SH-WRNN), which constrains weight matrices within a continuous parametric field instead of optimizing millions of localized discrete weights. When retrieving the weight matrix of the current layer, connection parameters are localized using latitude and longitude on a rectangular plane mapped from the continuous field. The latitudinal coordinate is specified by activated neurons from the previous layer, while the longitudinal coordinate is determined by keys generated from previous layer activations via matrix multiplication. By evaluating intersections on this map, the network dynamically extracts its connection weights on-the-fly. Empirical validation on MNIST demonstrates that under compact configurations of (32, 10, 10) and (32, 3, 10), SH-WRNN achieves robust accuracies of 91.05% and 81.54% within a single training epoch. Furthermore, we propose an asymmetric Surface Baking scheme. Upon convergence, the continuous weight field is baked once into a static parametric surface. By eliminating analytical spherical harmonics calculations during inference and reducing dynamic matrix extraction to high-speed localized memory slicing, this scheme achieves asymmetric algorithmic acceleration with negligible accuracy degradation. This paradigm shift bypasses GPU memory-bandwidth monopolies, opening a novel path to reshape the advantages of CPU computing. Code is available at this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-15 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.14614 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-15T23:54:14.704Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-15?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).