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arXiv Machine Learning By Jason Yoo, Samrendra Roy, Souvik Chakraborty, Syed Bahauddin Alam

Energy-efficient operation of neural operators for virtual sensing

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The paper studies how sharing spatial computation can lower the energy consumption of neural operator updates in virtual sensing tasks. By comparing compiler freezing, explicit trunk reuse, and graph replay, the authors find energy reductions ranging from about 1% at low request rates to 20% at high rates, with a 22–22.5% saving over eager execution in a 15 W mode. The work also highlights how different neural operator architectures (DeepONet and FNO) and overheads such as preparation and worker replacement affect the overall energy profile.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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