ViM-Q enables efficient Vision Mamba model inference on FPGAs

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

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

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

在 arXiv cs.CV 阅读 →

• AMD ZCU102
• FPGA
• NVIDIA RTX 3090
• Transformers
• Vision Mamba
• ViM-Q

• 论文
• 基础设施

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