What if a standard GPU suddenly consumed less power than its official idle value even under real workload? Recent measurements with the ฮฑZERO architecture suggest exactly that.
By establishing a self-organizing neural resonance field, energy appears to be generated or compensated directly within the system.
This effect contradicts conventional expectations in semiconductor physics and thermodynamics, opening the door to new physical models.
Instead of classical optimization, a stable and reproducible operating state emerges one that cannot be explained by established energy models.
The ฮฑZERO network requires no special hyperparameters or exotic hardware: it adapts to any environment and demonstrates a paradigm shift in our understanding of energy, information, and structural coupling.
Whether as a research impulse or an invitation for critical analysis, these results challenge conventional thinking.
The complete dataset, diagrams, and methods are documented in the latest preprint.
Highlights:
=> ๐๐ฉ ๐ญ๐จ 94% ๐๐ง๐๐ซ๐ ๐ฒ ๐ฌ๐๐ฏ๐ข๐ง๐ ๐ฌ ๐ฎ๐ง๐๐๐ซ ๐ซ๐๐๐ฅ-๐ฐ๐จ๐ซ๐ฅ๐ ๐๐จ๐ง๐๐ข๐ญ๐ข๐จ๐ง๐ฌ
=> ๐๐๐ฉ๐ซ๐จ๐๐ฎ๐๐ข๐๐ฅ๐ ๐๐๐๐๐๐ญ๐ฌ, ๐ฏ๐๐ฅ๐ข๐๐๐ญ๐๐ ๐๐ฒ ๐ฅ๐จ๐ง๐ -๐ญ๐๐ซ๐ฆ ๐ฆ๐๐๐ฌ๐ฎ๐ซ๐๐ฆ๐๐ง๐ญ๐ฌ
=> ๐๐๐ซ๐๐๐ข๐ ๐ฆ ๐ฌ๐ก๐ข๐๐ญ ๐ข๐ง ๐๐ง๐๐ซ๐ ๐ฒ ๐ฆ๐๐ง๐๐ ๐๐ฆ๐๐ง๐ญ ๐๐จ๐ซ ๐๐ ๐ก๐๐ซ๐๐ฐ๐๐ซ๐
Preprint => https://doi.org/10.5281/zenodo.15808984
#TrauthResearch #Resonance #NeuralNetwork #AI #DeepLearning
#AIhardware #GPUefficiency #Thermaldecoupling
#Experimentalphysics #Energyoptimization
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