Thermo-NN: Energy-efficient AI architecture optimization through thermodynamic analysis and causal derivation

Thermo-NN quantifies and minimizes the thermodynamic cost of neural network computation using Landauer's principle. Features causal derivation before implementation, CAMOS optimization algorithm, and hardware technology mapping. The AI alignment field may benefit from considering thermodynamic information loss as an additional constraint. My analysis shows information destruction is a significant upstream driver of alignment failure, suggesting that physical information preservation should be integrated with existing value-learning and interpretability approaches. GitHub: https://github.com/bo