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name: optimization-analysis-deployment description: "Use AIMET analysis, mixed precision, compression, debugging, export validation, and deployment artifact workflows after core quantization setup." metadata: disco-role: operating disable-model-invocation: true license: BSD 3-Clause

AIMET optimization, analysis, and deployment

Use this sub-skill when the user asks why accuracy dropped after quantization, how to use QuantAnalyzer or visualization outputs, how to choose mixed precision, how to use compression, or how to validate/export artifacts for target inference.

Read/run first

• Read API overview for compression, ONNX analysis, SeqMSE, AdaRound, and related signatures.
• Read workflows for the accuracy-debugging, compression, GenAILab, and deployment loops.
• Read backend compatibility to separate CPU-valid analysis from CUDA/provider, GenAILab, cluster, or target-runtime requirements.
• Read troubleshooting for accuracy, visualization, compression, GenAILab, credential, cluster, and export-artifact symptoms.
• Run inspect_export.py on an export directory before handing artifacts to target tooling.
• Route Qualcomm AI Hub, QAIRT, QNN, HTP, DLC, and SDK-command tasks to qualcomm-sdk-deployment.

Core workflow

1. Establish baselines. Compare FP32, high-bit-width, and target-bit-width QuantSim behavior before choosing remedies.
2. Separate weight and activation sensitivity. Raise one side's bit-width while holding the other side at target precision.
3. Apply the right PTQ remedy. Use BatchNorm folding/Cross-Layer Equalization/per-channel quantization/AdaRound/SeqMSE for weight issues; use range-setting, higher activation precision, or mixed precision for activation issues.
4. Analyze sensitive layers. Use QuantAnalyzer, per-layer sensitivity, histograms, and layer-output comparisons to identify the quantizer or op causing the drop.
5. Use mixed precision deliberately. Lite/manual/automatic mixed precision should be driven by measured sensitivity and target latency/accuracy constraints.
6. Compress when the target requires lower MACs or memory. Select compression ratios, apply Weight SVD/Spatial SVD/Channel Pruning, evaluate, then fine-tune if required.
7. Validate export artifacts. Confirm the ONNX model and AIMET encodings match before QNN/QAIRT/AI Hub or other target handoff.

Deployment boundaries

AIMET produces simulation/export artifacts. It does not prove target-device correctness by itself. For Qualcomm AI Runtime, QNN, QAIRT, HTP, DLC, or AI Hub tasks, first validate the AIMET export pair and then use qualcomm-sdk-deployment for the local SDK or AI Hub workflow.

Data and runtime boundaries

• Compression and accuracy examples often assume ImageNet-style evaluators; replace them with a tiny evaluator while developing.
• Visualization needs compatible Bokeh/HoloViews/HVPlot dependencies.
• LLM/VLM GenAILab recipe benchmarks are now covered by genai-lab, but they still require explicit model/dataset/credential/budget approval before execution.
• On-target execution requires external SDKs/devices or AI Hub credentials; local export validation is not target proof.

Expected answer shape

When answering optimization/deployment tasks, include:

• the baseline comparison being made;
• which quantizers/layers are being isolated;
• the selected AIMET remedy and why it matches weight/activation sensitivity;
• the evaluator/calibration data requirements;
• compression or mixed-precision constraints;
• exported artifacts and validation steps;
• explicit backend or target-runtime assumptions.