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UID:dac_DAC 2026_sess315_RESEARCH1357@linklings.com
SUMMARY:3D-DuRA: Accelerating Next-Resolution Generation via 3D Near/In-Me
 mory Architecture with Dual-Ring Sparse Attention
DESCRIPTION:Zecheng Zhou, Yuxiang Zhao, Xinyu Qu, Ruohang Xu, and Yufei Ma
  (Peking University)\n\nVisual Autoregressive (VAR) model, via innovative 
 next-resolution prediction, demonstrates significant potential of GPT-styl
 e AR models in image generation. However, due to its coarse-to-fine nature
 , the input token-map size grows dramatically with each step, resulting in
  excessive memory access and computational overhead. In this paper, we pro
 pose 3D-DuRA, an algorithm-architecture co-design based on a hybrid 3D nea
 r-memory and in-memory computing architecture equipped with dual-ring spar
 se attention, for efficient next-resolution visual-autoregressive generati
 on. Experimental results demonstrate that our proposed 3D-DuRA achieves 4.
 1× improvement in area efficiency compared with RTX 6000 Ada GPU, along wi
 th 3.5× and 9.1× speedups and 10.1× and 13.1× improvements in energy effic
 iency on Infinity2B and VAR-d36, respectively.\n\nTopics: Quantum\n\nTrack
 : DES6. Quantum Computing, Systems\n\nSession Chairs: Hokeun Kim (Arizona 
 State University); BaekGyu Kim (Daegu Gyeongbuk Institute of Science and T
 echnology); Xiang Chen (Peking University); Sangyeob Kim (Yonsei Universit
 y); Robert M. Radway (University of Pennsylvania); Prashanth Mohan (Univer
 sity of Pittsburgh); Xiaoxuan Yang (University of Virginia, Stanford Unive
 rsity); Ramtin Zand (University of South Carolina); Hadi Kamali (Universit
 y of Central Florida); Jiafeng Xie (Villanova University); Prabuddha Chakr
 aborty (University of Maine); Junichiro Kadomoto (University of Tokyo); an
 d Yosuke Ueno (RIKEN)\n\n
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