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name: qualcomm-sdk-deployment description: "Prepare AIMET exports for Qualcomm AI Hub, QAIRT/QNN conversion, profiling, inference, and SDK command generation." metadata: disco-role: operating disable-model-invocation: true license: BSD 3-Clause

Qualcomm SDK and AI Hub deployment

Use this sub-skill when the user asks how to send AIMET exports to Qualcomm AI Hub, QAIRT, QNN, HTP backends, DLC conversion, qairt-converter, qairt-quantizer, qnn-context-binary-generator, qnn-net-run, or AI Hub compile/profile/inference jobs.

Read/run first

• Read Qualcomm SDK workflows for artifact requirements and local/remote deployment sequences.
• Run inspect_export.py on the exported model directory before target handoff.
• Use qairt_command_builder.py to generate QAIRT/QNN command lines with local paths filled in.
• Use qai_hub_qnn_job.py for a self-contained AI Hub compile/profile entry point when qai_hub is installed and authenticated.
• Read model access and credentialed evaluation for AI Hub, AWS, and credential boundaries.

Core workflow

1. Validate AIMET artifacts. You need an exported .onnx model and matching AIMET .encodings file, or an ONNX QDQ model when the target flow expects QDQ.
2. Choose local SDK vs AI Hub. Local QAIRT/QNN commands require installed SDK binaries and libraries; AI Hub requires qai_hub credentials and supported devices.
3. Generate conversion commands. Feed the AIMET model and encodings into converter/quantizer commands; keep output DLC and context-binary paths explicit.
4. Compile/profile/infer. For local SDK, run context-binary generation and net-run with input lists. For AI Hub, submit compile/profile/inference jobs and record job URLs.
5. Check failure mode. Distinguish missing encodings, unsupported ops, channel order mismatch, provider/runtime mismatch, and missing target libraries.

Boundaries

AIMET export validation can be done locally. Target SDK execution cannot be proven unless the SDK, device/backend libraries, input list, and credentials are present. Do not claim target correctness from a successful AIMET QuantSim export alone.

Expected answer shape

For deployment tasks, include the input artifact paths, target device/runtime, local SDK or AI Hub path, generated commands or API calls, expected output files, and exact credential/SDK assumptions.