标题： 具有k尺度量化和重新参数化的视觉Mamba的后训练量化 显示英文标题

标题： Post-Training Quantization for Vision Mamba with k-Scaled Quantization and Reparameterization

摘要： Mamba 模型采用结构化状态空间模型（SSM），具有线性时间复杂度，并展现出显著潜力。Vision Mamba（ViM）通过引入双向 SSM 和补丁嵌入扩展了这一框架至视觉任务，在性能上超过了基于 Transformer 的模型。 尽管模型量化对于高效计算至关重要，但现有工作仅关注原始的 Mamba 模型，尚未将其应用于 ViM。此外，它们忽略了量化 SSM 层，而该层是 Mamba 的核心部分，由于其固有结构，粗暴的量化可能导致严重的误差传播。 本文专注于 ViM 的后训练量化（PTQ）。我们通过三种核心技术解决了相关问题：1）针对线性和卷积层的 k-scale 标记量化方法，2）简化隐藏状态量化的重新参数化技术，以及 3）一种通过整合操作减少计算开销的因素确定方法。通过这些方法，可以减轻由 PTQ 引发的误差。 ImageNet-1k 上的实验结果显示，由于 PTQ 导致的准确率下降仅为 0.8%-1.2%，这凸显了我们方法的有效性。 显示英文摘要

摘要： The Mamba model, utilizing a structured state-space model (SSM), offers linear time complexity and demonstrates significant potential. Vision Mamba (ViM) extends this framework to vision tasks by incorporating a bidirectional SSM and patch embedding, surpassing Transformer-based models in performance. While model quantization is essential for efficient computing, existing works have focused solely on the original Mamba model and have not been applied to ViM. Additionally, they neglect quantizing the SSM layer, which is central to Mamba and can lead to substantial error propagation by naive quantization due to its inherent structure. In this paper, we focus on the post-training quantization (PTQ) of ViM. We address the issues with three core techniques: 1) a k-scaled token-wise quantization method for linear and convolutional layers, 2) a reparameterization technique to simplify hidden state quantization, and 3) a factor-determining method that reduces computational overhead by integrating operations. Through these methods, the error caused by PTQ can be mitigated. Experimental results on ImageNet-1k demonstrate only a 0.8-1.2\% accuracy degradation due to PTQ, highlighting the effectiveness of our approach.