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Research questionHow can Vision Transformers avoid LayerNorm’s global reduction on edge accelerators without expensive retraining?LayerNorm requires a global reduction that creates computation and data-movement overhead on edge accelerators. Replacing it with a single scalar function across layers may not match the different behaviors of individual layers and can require costly retraining.
AI
Computer Vision
Inference Optimization
Machine Learning
Neural and Evolutionary Computing
Small / On-device Models
Technology
Latest papersRecent research connected to this question, newest first.Evolving Layer-Specific Scalar Functions for Hardware-Aware Transformer AdaptationThe evidence concerns ViT-B and ViT-L models for ImageNet-1K classification, using layer-specific scalar replacements derived from pretrained weights and post-training realignment. Reported results include 20-epoch adaptation and reduced arithmetic complexity and off-chip memory traffic, but no broader tasks or hardware configurations are established.research paper · Sep 4, 2026
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