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Bringing AI to the Heterogeneous Edge with WebAssembly & ONNX

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Demonstrates running ONNX models as WASM modules: converts a SqueezeNet model to ONNX, updates onnxruntime-rs and adds an ONNX backend to waCNN, then compiles a component for wasm-time/runwasi and runs local inference. The prototype loads a ~45 MB model into Wasm, preprocesses a 224×224 image, runs FP32 inference and softmax, and returns a 99% class score for a dog — code and PRs for runtime and wasm nn are upstreaming.

AI-written summary · about

  1. 0:00Introduction
  2. 4:53Edge AI challenges
  3. 8:02WebAssembly and ONNX solutions
  4. 11:34Live demo: Wasm & ONNX inferencing
  5. 19:58Future of WasmNN & model optimization
  6. 27:45Q&A
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