EaqVLA: 視覚-言語-行動モデルのための符号化整合量子化
EaqVLA: Encoding-aligned Quantization for Vision-Language-Action Models
VLAモデルの量子化におけるトークン不整合問題を分析し、符号化整合を考慮した混合精度量子化フレームワークを提案。既存手法より高い量子化性能と加速を実現。
著者: Feng Jiang, Zihao Zheng, Xiuping Cui, Maoliang Li, JIayu Chen, Xiang Chen
分類: cs.CV, cs.LG
原文アブストラクト
With the development of Embodied Artificial intelligence, the end-to-end control policy such as Vision-Language-Action (VLA) model has become the mainstream. Existing VLA models faces expensive computing/storage cost, which need to be optimized. Quantization is considered as the most effective method which can not only reduce the memory cost but also achieve computation acceleration. However, we find the token alignment of VLA models hinders the application of existing quantization methods. To address this, we proposed an optimized framework called EaqVLA, which apply encoding-aligned quantization to VLA models. Specifically, we propose an complete analysis method to find the misalignment in various granularity. Based on the analysis results, we propose a mixed precision quantization with the awareness of encoding alignment. Experiments shows that the porposed EaqVLA achieves better quantization performance (with the minimal quantization loss for end-to-end action control and xxx times acceleration) than existing quantization methods.