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

2025-08-04

AI Summary

Vision Mamba suffers from poor parallelism, memory bandwidth bottlenecks, and low GPU utilization on edge devices due to its sequential scan operation. To address these challenges, this work proposes an end-to-end hardware–software co-optimization framework. We design a dedicated systolic scan array to accelerate the state-space model’s sequential scanning path in hardware, enabling fine-grained parallelism. Additionally, we introduce a hardware-friendly hybrid quantization scheme (FP16/INT8 co-quantization) that compresses weights and intermediate activations without any accuracy loss. Experimental results demonstrate that our approach reduces inference latency by 42% and memory footprint by 57% compared to standard Transformer-based inference. Consequently, it significantly improves throughput and energy efficiency on edge AI chips. This work establishes a scalable hardware acceleration paradigm for Mamba-style models in resource-constrained deployment scenarios.

Technology Category

• Machine Learning: Hardware-aware ML
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• Reduce computational and memory demands in vision models
• Enable efficient deployment of Vision Mamba on edge devices
• Improve hardware efficiency without sacrificing model accuracy

Innovation

• Systolic scan array maximizes parallelism efficiency
• Hybrid quantization reduces memory usage effectively
• End-to-end accelerator optimizes edge device deployment