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arXiv AI By Zhanwei Wu (Qualcomm Technologies, Inc), Jianhao Su (Qualcomm Technologies, Inc), Brian Zhang (Qualcomm Technologies, Inc), ShengTing Huang (Qualcomm Technologies, Inc), Weidong Feng (Qualcomm Technologies, Inc), Gang Liu (Qualcomm Technologies, Inc), Zhiguo Li (Qualcomm Technologies, Inc), Wei Huang (Qualcomm Technologies, Inc), Li Tan (Qualcomm Technologies, Inc), Wanjun Li (Qualcomm Technologies, Inc), Zejiong Teng (Qualcomm Technologies, Inc), Changgeng Zhong (Qualcomm Technologies, Inc), Guanping Wu (Qualcomm Technologies, Inc), Han-Hsing Tu (Independent Researcher)

AIPC: Evidence-Guided Agent Workflows for PyTorch Project Deployment

Read the original on arXiv AI →

The paper introduces AIPC, an AI‑powered method that uses evidence‑guided agents to automate the deployment of PyTorch inference projects on dedicated accelerators such as on‑device NPUs. AIPC orchestrates model adaptation, artifact building, on‑target execution, accuracy repair, and performance optimization through structured skills and toolchain evidence, addressing cross‑layer challenges in model formats, quantization, and runtime interfaces. Experiments on Qualcomm QAIRT demonstrate significant speedups and high accuracy across vision, speech, LLM, and diffusion models, with some runs requiring no human intervention.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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