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Arxiv
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AHCPTQ: Accurate and Hardware-Compatible Post-Training Quantization for Segment Anything Model
- Researchers have introduced AHCPTQ, a Post-Training Quantization (PTQ) method to address challenges in the Segment Anything Model (SAM) for efficient deployment.
- AHCPTQ employs Hybrid Log-Uniform Quantization (HLUQ) for managing post-GELU activations and Channel-Aware Grouping (CAG) to address inter-channel variation in SAM.
- The combination of HLUQ and CAG in AHCPTQ enhances quantization effectiveness, hardware efficiency, and compatibility for efficient hardware execution.
- AHCPTQ demonstrates significant performance improvements over its floating-point counterpart, achieving 36.6% mAP on instance segmentation with DINO detector, along with speedup and energy efficiency gains in FPGA implementation.
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