A 28nm 0.22μj/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, Manto Yung,Dong Zhang,Xijie Huang,Shih-Yang Liu, Yongkun Wu,Fengshi Tian,Chi-Ying Tsui,Fengbin Tu,Kwang-Ting Cheng

IEEE International Solid-State Circuits Conference（2025）

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Key words

Energy Consumption,Decoding,Sparsity,Receptive Field,Transformer Model,Computational Overhead,CNN Model,Open Reduction,Semantic Segmentation Task,Hardware Accelerators,Language Processing Tasks,External Access,Left Matrix,Convolutional Weights,Backbone Segments

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