most citedA 28nm 0.22μJ/token memory-compute-intensity-aware CNN-Transformer accelerator with hybrid-attention-based layer-fusion and cascaded pruning for semantic-segmentation

11 citations · 11 across the 2 of their papers we have counts for

collaborators

2 papers

eess.IV2026★ 11 cited

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 +14

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 atte…

cs.AR2025

CompAir: Synergizing Complementary PIMs and In-Transit NoC Computation for Efficient LLM Acceleration

Hongyi Li, Songchen Ma, Huanyu Qu +5

The rapid advancement of Large Language Models (LLMs) has revolutionized various aspects of human life, yet their immense computational and energy demands pose significant challeng…