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Model Explorer
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Bringing Visual Graph Debugging to Custom Edge Compilers with Model Explorer
Machine learning models require compilation steps such as quantization, graph lowering, operator fusion, and hardware delegation when deployed to edge devices. During deployment, engineers must verify structural integrity, full accelerator delegation, and cycle-level performance bottlenecks. Arm built open-source adapters for Google AI Edge Model Explorer to visualize Arm-specific intermediate representations including VGF and TOSA. Inspecting a VGF graph of a PyTorch Neural Super Sampling model uncovered unintended TRANSPOSE operators introduced by a backend change. For an Ultralytics YOLO model, split-pane view confirmed complete NPU delegation without CPU fallback, and per-node cycle-accurate tracing with color-coded heatmaps identified a single TRANSPOSE node as the primary latency bottleneck.
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