I-Segmenter: New Integer-Only Vision Transformer Framework for Efficient Semantic Segmentation
Researchers have introduced I-Segmenter, the first fully integer-only Vision Transformer (ViT) framework designed for semantic segmentation on resource-constrained devices. The system builds on the existing Segmenter architecture, replacing all floating-point operations with integer-only equivalents and introducing a novel activation function called λ-ShiftGELU to handle quantization instability. The work addresses a key barrier to deploying powerful AI vision models on edge hardware, where memory and compute budgets are tight.
I-Segmenter, accepted by the Journal of Systems Architecture, presents the first end-to-end integer-only quantization framework for ViT-based semantic segmentation models. The core challenge it addresses is that ViT segmentation pipelines are particularly vulnerable to quantization errors, which compound across deep encoder-decoder stacks when precision is reduced from 32-bit floating point. To counter this, the authors introduce λ-ShiftGELU, a novel activation function designed to better handle the long-tailed activation distributions that cause uniform quantization to fail. Additional architectural changes include removing the L2 normalization layer and substituting bilinear interpolation in the decoder with nearest-neighbor upsampling, ensuring no floating-point operations remain anywhere in the computational graph. Experiments show I-Segmenter achieves accuracy within 5.1% of its full-precision baseline on average, while reducing model size by up to 3.8x and delivering up to 1.2x faster inference on optimized runtimes. Notably, the framework performs competitively even under one-shot post-training quantization using a single calibration image, suggesting strong practical utility without extensive calibration data.
What's missing
Comparisons to other quantization approaches for ViT segmentation models beyond the FP32 baseline are not detailed in the abstract.
What different sources said
- arXiv cs.AICenter
I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation
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