I-Segmenter: New Integer-Only Vision Transformer Framework for Efficient Semantic Segmentation
Researchers introduced I-Segmenter, the first fully integer-only Vision Transformer (ViT) framework designed for semantic segmentation on resource-constrained devices. The framework replaces floating-point operations with integer-only counterparts and introduces a novel activation function called λ-ShiftGELU to stabilize training and inference. The approach achieves up to 3.8x model size reduction and 1.2x faster inference while maintaining reasonable accuracy compared to standard floating-point models, making it practical for real-world deployment on edge devices.
I-Segmenter addresses a significant challenge in deploying Vision Transformers for semantic segmentation on devices with limited computational resources. The framework systematically converts floating-point operations to integer-only equivalents throughout the entire encoder-decoder pipeline, which typically accumulates quantization errors that degrade performance. The researchers propose λ-ShiftGELU, a novel activation function designed to handle long-tailed activation distributions more effectively under uniform quantization. Additional optimizations include removing L2 normalization and replacing bilinear interpolation with nearest neighbor upsampling. Experimental results demonstrate that I-Segmenter achieves accuracy within 5.1% of its full-precision baseline on average, while delivering substantial efficiency gains: up to 3.8x reduction in model size and up to 1.2x faster inference with optimized runtimes. Notably, the framework performs competitively even with post-training quantization using only a single calibration image, demonstrating practical viability for deployment scenarios.
What's missing
The paper does not specify which semantic segmentation datasets were used for evaluation, the specific hardware platforms tested, or comparisons with other quantization approaches for ViT-based segmentation models.
What different sources said
- arXiv cs.AICenter
I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation
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