The LL-ViT model is a Vision Transformer designed specifically for deployment on edge devices such as FPGAs. It incorporates Look Up Table neurons, a technology that enables significant reductions in both memory usage and computational demands. These improvements address key challenges associated with running Vision Transformers on resource-constrained hardware. By lowering the resource requirements, LL-ViT presents a promising solution for efficient edge inference, meeting the growing demand for deploying AI models outside traditional data centers. The model’s design and benefits have been highlighted in recent research, emphasizing its potential impact in the field of computer vision on edge platforms. This development aligns with ongoing efforts to optimize AI models for practical, real-world applications where computational efficiency is critical.