AIPC: Agent-Based Automation for AI Model Deployment with Qualcomm AI Runtime

A team of researchers has introduced AIPC (AI Porting Conversion), a novel framework that uses AI agents to automate the complex, multi-stage process of deploying AI models to specialized hardware like Qualcomm's AI Runtime (QAIRT). The system addresses a major bottleneck in edge AI: converting models from frameworks like PyTorch into optimized formats for mobile and edge devices, which typically requires days of manual engineering work and deep hardware expertise. AIPC breaks this workflow into standardized stages—including model conversion, operator compatibility handling, quantization calibration, and accuracy validation—and injects deployment knowledge through reusable Agent Skills and helper scripts.

In practical tests targeting Qualcomm's QNN/SNPE inference engines, AIPC demonstrated dramatic efficiency gains. For structurally regular vision models, the system completed full deployment pipelines in just 7-20 minutes, with indicative API costs ranging from $0.7 to $10—representing approximately 90% reductions in both time and cost compared to manual approaches. The framework provides practical support for execution, failure localization, and bounded repair, though fully automated deployment for complex models with dynamic shapes or autoregressive structures remains challenging. This represents a significant step toward democratizing edge AI deployment and could accelerate adoption of on-device AI applications.