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ViM-Q enables efficient Vision Mamba model inference on FPGAs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed ViM-Q, a novel algorithm-hardware co-design specifically for accelerating Vision Mamba (ViM) model inference on FPGAs. This approach tackles challenges in quantizing dynamic activation outliers and adapting SSM computation for FPGA architectures. ViM-Q integrates a custom 4-bit weight quantization with a hardware accelerator featuring a linear engine and a pipelined SSM engine, enabling runtime configuration for diverse ViM models. Tests on an AMD ZCU102 FPGA demonstrated significant speedup and energy efficiency gains compared to a GPU baseline for low-batch inference. AI

IMPACT Enables efficient deployment of Vision Mamba models on resource-constrained edge devices.

RANK_REASON Academic paper detailing a new algorithm-hardware co-design for model inference. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

ViM-Q enables efficient Vision Mamba model inference on FPGAs

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Academic paper detailing a new algorithm-hardware co-design for model inference. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Shengzhe Lyu, Yuhan She, Patrick S. Y. Hung, Ray C. C. Cheung, Weitao Xu ·

    ViM-Q: Scalable Algorithm-Hardware Co-Design for Vision Mamba Model Inference on FPGA

    arXiv:2605.01935v1 Announce Type: cross Abstract: Vision Mamba (ViM) models offer a compelling efficiency advantage over Transformers by leveraging the linear complexity of State Space Models (SSMs), yet efficiently deploying them on FPGAs remains challenging. Linear layers strug…

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