🤖 AI Summary
This work addresses the complexity and heavy reliance on expert knowledge in deploying edge AI models, particularly when adapting to hardware-specific inference runtimes such as Qualcomm QNN/SNPE. To tackle this challenge, the authors propose AIPC—the first AI agent–based automated deployment framework—that decomposes the deployment pipeline into standardized, verifiable stages. By integrating domain expertise through agent skills, auxiliary scripts, and iterative stage-wise validation loops, AIPC enables knowledge-guided verification and limited repair capabilities. Experimental results demonstrate that AIPC can complete end-to-end deployment of canonical vision models from PyTorch to QNN/SNPE within 7–20 minutes (at an API cost of approximately \$0.7–10 per run), effectively diagnose failures in complex models, and provide actionable repair suggestions, thereby substantially reducing the need for manual intervention.
📝 Abstract
Edge AI model deployment is a multi-stage engineering process involving model conversion, operator compatibility handling, quantization calibration, runtime integration, and accuracy validation. In
practice, this workflow is long, failure-prone, and heavily dependent on deployment expertise, particularly when targeting hardware-specific inference runtimes. This technical report presents AIPC (AI
Porting Conversion), an AI agent-driven approach for constrained automation of AI model deployment. AIPC decomposes deployment into standardized, verifiable stages and injects deployment-domain knowledge
into agent execution through Agent Skills, helper scripts, and a stage-wise validation loop. This design reduces both the expertise barrier and the engineering time required for hardware deployment.
Using Qualcomm AI Runtime (QAIRT) as the primary scenario, this report examines automated deployment across representative vision, multimodal, and speech models. In the cases covered here, AIPC can
complete deployment from PyTorch to runnable QNN/SNPE inference within 7-20 minutes for structurally regular vision models, with indicative API costs roughly in the range of USD 0.7-10. For more complex
models involving less-supported operators, dynamic shapes, or autoregressive decoding structures, fully automated deployment may still require further advances, but AIPC already provides practical support
for execution, failure localization, and bounded repair.