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UID:dac_DAC 2026_sess317_RESEARCH2271@linklings.com
SUMMARY:PeFoo-L: A General Framework for Preconditioned Forward-Only Optim
 izer Enabling LLM Fine-tuning on the Edge
DESCRIPTION:Yuanfang Wang, Yu Li, and Zhinan Qin (Fudan University)\n\nFin
 e-tuning Large Language Models (LLMs) on resource-constrained edge devices
  is a critical but challenging task, primarily due to the prohibitive memo
 ry and computational costs of backpropagation. While forward-only optimize
 rs like MeZO mitigate these costs by eliminating the backward pass, they o
 ften suffer from slow and unstable convergence. To address this limitation
 , we introduce PeFoo, which integrates a carefully designed preconditionin
 g strategy into the forward-only paradigm. Furthermore, we propose PeFoo-L
  to counteract the memory overhead introduced by the preconditioner. This 
 approach constrains preconditioner storage and weight updates to a single 
 layer per iteration, reducing the overall memory footprint and data traffi
 c.\n\nTrack: EDA7-II. Physical Design and Verification, Design\n\nSession 
 Chairs: Christian Pilato (Politecnico di Milano); Mohsen Imani (University
  of California, Irvine); Flavio Ponzina (San Diego State University); Amin
  Farshidi (Cadence Design Systems, Inc.); Yonggan Fu (Georgia Institute of
  Technology, NVIDIA); Dimitrios Tsaras (Huawei, Hong Kong University of Sc
 ience and Technology (HKUST)); Anh Nguyen (Fujitsu Limited); Ruiyang Qin (
 Villanova University); Jiliang Zhang (Hunan University); Meng Li (Peking U
 niversity); and Rassul Bairamkulov (Advanced Micro Devices (AMD))\n\n
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