Bringing Visual Graph Debugging to Custom Edge Compilers with Model Explorer

Arm has extended Google AI Edge Model Explorer with open-source adapters for TOSA, VGF, and PTE formats, giving developers visual tools to inspect and optimize machine-learning models deployed on edge hardware. The integration addresses common deployment challenges including detecting unintended graph changes during export, verifying whether workloads are fully delegated to NPUs or GPUs rather than falling back to CPUs, and locating cycle-level performance bottlenecks. In examples involving a Neural Super Sampling model, a YOLO vision model, and Arm Ethos-U NPU profiling, Model Explorer helped engineers identify unexpected transpose operations, compare CPU and NPU graph structures side by side, and reveal a transpose node as a primary latency source through trace overlays. The tools can be used locally through PyPI or GitHub alongside built-in adapters for LiteRT, PyTorch, and Hugging Face Transformers.

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