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ST Edge AI Core v3.0: GTCRN, LiSenNet: "Incomplete symbolic shape inference"

giuug
Associate II

‎2026-02-05 1:12 AM - last edited on ‎2026-02-05 1:52 AM by

Hi everyone,

I’m currently working with the STM32N6 and the new ST Edge AI Core v3.0, trying to deploy some Speech Enhancement models. I’m specifically targeting architectures like LiSenNet, GTCRN, and UL-UNAS because they are theoretically designed for embedded hardware..

However, the workflow with STM-model-zoo-services is giving me a hard time:

  1. Analyze Failure: After training -->best_model.onnx --> stedgeai.exe analyze -m best_model.onnx --> fails immediately.

  2. Quantization Issues: When I try to quantize, I consistently get an "Incomplete symbolic shape inference" error. It seems the compiler struggles with dynamic shapes or specific operators even though these models should be compatible.

Is the ST Edge AI Core v3.0 limited to simpler architectures like TCN for Speech Enhancement, or is there a way to make GTCRN work?

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