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UID:dac_DAC 2026_sess315_RESEARCH2410@linklings.com
SUMMARY:CAPA: Convertibility-Aware Mixed-Precision Accelerator for Asymmet
 ric GEMM in Weight-Only Quantized LLMs
DESCRIPTION:Jihyun Moon, Insu Choi, Seungchan Lee, and Joon-Sung Yang (Yon
 sei University)\n\nFP-INT GEMM accelerators for weight-only quantized larg
 e language models suffer from large partial sum (psum) overhead and a lack
  of precision-convertibility, limiting scalability. To mitigate this, we p
 ropose CAPA, an accelerator achieving high efficiency and versatility. (1)
  CAPA introduces Hybrid Delta Block Floating Point (HDBFP), a novel INT-ba
 sed format that reduces psum overhead, preserves accuracy, and integrates 
 BF16/FP16 arithmetic. (2) CAPA adopts Weight Decomposition to unify INT8 a
 nd INT4 arithmetic, enabling parallel processing. We demonstrate CAPA atta
 ins identical GPU accuracy, improving area efficiency by 3.98x and power e
 fficiency by 4.38x over the FP-FP baseline.\n\nTopics: Quantum\n\nTrack: D
 ES6. Quantum Computing, Systems\n\nSession Chairs: Hokeun Kim (Arizona Sta
 te University); BaekGyu Kim (Daegu Gyeongbuk Institute of Science and Tech
 nology); Xiang Chen (Peking University); Sangyeob Kim (Yonsei University);
  Robert M. Radway (University of Pennsylvania); Prashanth Mohan (Universit
 y of Pittsburgh); Xiaoxuan Yang (University of Virginia, Stanford Universi
 ty); Ramtin Zand (University of South Carolina); Hadi Kamali (University o
 f Central Florida); Jiafeng Xie (Villanova University); Prabuddha Chakrabo
 rty (University of Maine); Junichiro Kadomoto (University of Tokyo); and Y
 osuke Ueno (RIKEN)\n\n
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