FTerViT: Fully Ternary Vision Transformer
Researchers have developed FTerViT, a fully ternary Vision Transformer that compresses all weight matrices and normalization parameters. This approach significantly reduces the model's memory footprint, making it more feasible for deployment on resource-constrained devices like microcontrollers. FTerViT achieves competitive accuracy on ImageNet while offering substantial compression compared to standard floating-point models. AI
IMPACT Enables more efficient deployment of advanced vision models on low-power edge devices.