ViM-Q delivers 4.96x speedup and 59.8x energy efficiency for Vision Mamba inference on FPGA versus a quantized GPU baseline using dynamic activation quantization, per-block APoT weights, and a pipelined SSM engine.
SVDQuant: Absorbing outliers by low-rank com- ponent for 4-bit diffusion models,
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ViM-Q: Scalable Algorithm-Hardware Co-Design for Vision Mamba Model Inference on FPGA
ViM-Q delivers 4.96x speedup and 59.8x energy efficiency for Vision Mamba inference on FPGA versus a quantized GPU baseline using dynamic activation quantization, per-block APoT weights, and a pipelined SSM engine.