FTerViT: Fully Ternary Vision Transformer

ArXi:2605.21171v1 Announce Type: new Ternary Vision Transformers offer substantial model compression,. however. state-of-the-art methods only ternarize the encoder layers, leaving patch embeddings, LayerNorm parameters, and classifier heads in full precision. In compact models targeting resource-constrained processors, such as microcontrollers, these remaining full-precision components determine the total memory footprint, severely limiting deployment efficiency and on-device feasibility. In this work, we.