标题： Mamba-X：面向边缘计算设备的端到端视觉Mamba加速器 显示英文标题

标题： Mamba-X: An End-to-End Vision Mamba Accelerator for Edge Computing Devices

摘要： Transformer在语言建模中已被证明是有效的，但其计算和内存需求随着输入序列长度的增加呈二次增长，这限制了其应用。状态空间模型（SSMs）通过将注意力复杂度从$O(L^2)$降低到$O(L)$，同时降低整体内存消耗，提供了一个有前景的替代方案。Vision Mamba将SSM方法适应于计算机视觉任务，实现了比传统Transformer模型更低的延迟和内存消耗。然而，由于其顺序扫描操作，将Vision Mamba部署在边缘设备上具有挑战性，这会阻碍GPU效率。我们提出了Mamba-X，这是一种端到端的Vision Mamba加速器，包括一个脉动扫描阵列以最大化并行性并最小化内存流量，以及一种混合的、适合硬件的量化技术，在不牺牲准确性的情况下减少内存使用并提高硬件效率。 显示英文摘要

摘要： Transformers have proven effective in language modeling but are limited by high computational and memory demands that grow quadratically with input sequence length. State space models (SSMs) offer a promising alternative by reducing attention complexity from $O(L^2)$ to $O(L)$ while also lowering overall memory consumption. Vision Mamba adapts the SSM approach for computer vision tasks, achieving lower latency and memory consumption than traditional transformer models. However, deploying Vision Mamba on edge devices is challenging due to its sequential scan operations, which hinder GPU efficiency. We propose Mamba-X, an end-to-end Vision Mamba accelerator that includes a systolic scan array to maximize parallelism and minimize memory traffic, along with a hybrid, hardware-friendly quantization technique to reduce memory usage and improve hardware efficiency without sacrificing accuracy.