Genetic programming evolves heterogeneous layer-specific scalar functions to approximate layer normalization in pre-trained ViTs, capturing 91.6% variance versus 70.2% for uniform baselines and recovering 84.25% ImageNet Top-1 accuracy after 20 epochs of adaptation.
C- transformer: A 2.6-18.1 µJ/Token homogeneous DNN-transformer/spiking-transformer processor with big-little network and implicit weight generation for large language models
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Evolving Layer-Specific Scalar Functions for Hardware-Aware Transformer Adaptation
Genetic programming evolves heterogeneous layer-specific scalar functions to approximate layer normalization in pre-trained ViTs, capturing 91.6% variance versus 70.2% for uniform baselines and recovering 84.25% ImageNet Top-1 accuracy after 20 epochs of adaptation.