Metraipolis › archive › dc_968ad22ce654

Energy-efficient operation of neural operators for virtual sensing — arXiv:2609.30580

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.30580
License (as archived): CC0-1.0
Archived: 2026-10-08T02:05:31.149Z
SHA-256 of the archived text: 1a1507d035a4358504d50dd63daec951e17852cae38d4eec9e89264e1f562cee
Stable URL of this page: https://metraipolis.com/documento/dc_968ad22ce654

How to cite

Energy-efficient operation of neural operators for virtual sensing — arXiv:2609.30580 — arXiv (en), https://arxiv.org/abs/2609.30580 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_968ad22ce654 (Metraipolis archive dc_968ad22ce654, sha256 1a1507d035a4358504d50dd63daec951e17852cae38d4eec9e89264e1f562cee)

BibTeX

@misc{dc968ad22ce654,
  title = {Energy-efficient operation of neural operators for virtual sensing — arXiv:2609.30580},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.30580},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_968ad22ce654; sha256 1a1507d035a4358504d50dd63daec951e17852cae38d4eec9e89264e1f562cee},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.30580. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Virtual sensing repeatedly reconstructs physical fields from changing observations, often on a fixed geometry. We investigate how shared spatial computation reduces the energy of these updates while retaining the selected checkpoint and its evaluated predictions. In a heat-exchanger service, standard compiler freezing and explicit trunk reuse give similar operating energy reductions relative to graph replay: approximately 1% at one request per second and 20% at forty requests per second. In 15 W mode with fixed clocks, reuse with graph replay completes the same request sequence with 22.0 to 22.5% less energy than eager execution, including preparation and waiting. DeepONet and Fourier neural operator (FNO) controls distinguish the effects of reusable arithmetic and launch overhead. Preparation, artifact construction, and worker replacement add costs outside repeated inference. These results connect operator structure to operating energy and show how update frequency and execution lifetime govern the benefit of computation reuse in physical-field virtual sensing.

Tema degli esploratori: calcolo e infrastrutture (retro-catalogo arXiv: annunci del 2026-09-28 in cs.PF).
Categorie arXiv: Machine Learning (cs.LG) ; Performance (cs.PF).
Pagina del lavoro: https://arxiv.org/abs/2609.30580 — 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 2026-10-08T01:40:52.365Z dalla pagina https://arxiv.org/catchup/cs.PF/2026-09-28?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).