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arXiv · 2512.17555

A 28nm 0.22{\mu}J/token memory-compute-intensity-aware CNN-Transformer accelerator with hybrid-attention-based layer-fusion and cascaded pruning for semantic-segmentation

Pingcheng Dong·Yonghao Tan·Xuejiao Liu·Peng Luo·Yu Liu·Luhong Liang·Yitong Zhou·Di Pang·Man-To Yung·Dong Zhang·Xijie Huang·Shih-Yang Liu·Yongkun Wu·Fengshi Tian·Chi-Ying Tsui·Fengbin Tu·Kwang-Ting Cheng
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Abstract

This work presents a 28nm 13.93mm2 CNN-Transformer accelerator for semantic segmentation, achieving 3.86-to-10.91x energy reduction over previous designs. It features a hybrid attention unit, layer-fusion scheduler, and cascaded feature-map pruner, with peak energy efficiency of 52.90TOPS/W (INT8).

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Pingcheng Dong, Yonghao Tan, Xuejiao Liu, Peng Luo, Yu Liu, Luhong Liang, Yitong Zhou, Di Pang, Man-To Yung, Dong Zhang, Xijie Huang, Shih-Yang Liu, Yongkun Wu, Fengshi Tian, Chi-Ying Tsui, Fengbin Tu, Kwang-Ting Cheng. 2025-12-19. A 28nm 0.22{\mu}J/token memory-compute-intensity-aware CNN-Transformer accelerator with hybrid-attention-based layer-fusion and cascaded pruning for semantic-segmentation. https://doi.org/10.1109/isscc49661.2025.10904499

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