@Article{cmc.2025.072735,
AUTHOR = {Rayeesa Mehmood, Sergei Koltcov, Anton Surkov, Vera Ignatenko},
TITLE = {Modeling Pruning as a Phase Transition: A Thermodynamic Analysis of Neural Activations},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {86},
YEAR = {2026},
NUMBER = {3},
PAGES = {--},
URL = {http://www.techscience.com/cmc/v86n3/65482},
ISSN = {1546-2226},
ABSTRACT = {Activation pruning reduces neural network complexity by eliminating low-importance neuron activations, yet identifying the critical pruning threshold—beyond which accuracy rapidly deteriorates—remains computationally expensive and typically requires exhaustive search. We introduce a thermodynamics-inspired framework that treats activation distributions as energy-filtered physical systems and employs the free energy of activations as a principled evaluation metric. Phase-transition–like phenomena in the free-energy profile—such as extrema, inflection points, and curvature changes—yield reliable estimates of the critical pruning threshold, providing a theoretically grounded means of predicting sharp accuracy degradation. To further enhance efficiency, we propose a renormalized free energy technique that approximates full-evaluation free energy using only the activation distribution of the unpruned network. This eliminates repeated forward passes, dramatically reducing computational overhead and achieving speedups of up to <mml:math id="mml-ieqn-1"><mml:mn>550</mml:mn><mml:mo>×</mml:mo></mml:math> for MLPs. Extensive experiments across diverse vision architectures (MLP, CNN, ResNet, MobileNet, Vision Transformer) and text models (LSTM, BERT, ELECTRA, T5, GPT-2) on multiple datasets validate the generality, robustness, and computational efficiency of our approach. Overall, this work establishes a theoretically grounded and practically effective framework for activation pruning, bridging the gap between analytical understanding and efficient deployment of sparse neural networks.},
DOI = {10.32604/cmc.2025.072735}
}