{"ID":2827088,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.17555","arxiv_id":"2512.17555","title":"A 28nm 0.22μJ/token memory-compute-intensity-aware CNN-Transformer accelerator with hybrid-attention-based layer-fusion and cascaded pruning for semantic-segmentation","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).","short_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).","url_abs":"https://arxiv.org/abs/2512.17555","url_pdf":"https://arxiv.org/pdf/2512.17555v2","authors":"[\"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\"]","published":"2025-12-19T13:24:06Z","proceeding":"eess.IV","tasks":"[\"eess.IV\"]","methods":"[\"Transformer\",\"Convolutional Neural Network\"]","has_code":false}