minAction.net: Energy-First Neural Architecture Design -- From Biological Principles to Systematic Validation
Modern machine learning optimizes for accuracy without explicit treatment of internal computational cost, even though physical and biological systems operate under intrinsic energy constraints. We evaluate energy-aware learning across 2,203 experiments spanning vision, text, neuromorphic, and physiological datasets with 10 seeds per configuration and factorial statistical analysis. Three findings emerge. First, architecture alone explains negligible variance in accuracy (partial eta^2 = 0.001), while the architecture x dataset interaction is large (partial eta^2 = 0.44, p < 0.001), demonstrating that optimal architecture depends critically on task modality and rejecting the assumption of ...
| Authors | Martin G. Frasch |
|---|---|
| Primary category | cs.LG |
| Published | 27 April 2026 |
| Last updated at source | 29 April 2026 |
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