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.
Eyeriss: An Energy- Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A symbolic polyhedral methodology estimates energy for nested loops on processor arrays independently of problem size, enabling faster design exploration than simulation.
citing papers explorer
-
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.
-
Symbolic Polyhedral-Based Energy Analysis for Nested Loop Programs
A symbolic polyhedral methodology estimates energy for nested loops on processor arrays independently of problem size, enabling faster design exploration than simulation.