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mil-tokyo/webdnn ★ 1999 149 APPLICATION

WebDNN is a browser-based neural network inference engine that executes ONNX models directly on the client side. It provides four backends—CPU, WebAssembly, WebGL (1/2), and WebGPU—allowing models to run on a wide range of hardware. The repository includes a Python graph transpiler for offline model optimization (shader generation, operator fusion, 8-bit quantization) and a TypeScript runtime with a modular operator registration system. While the runtime is application-oriented (library for web ML inference), the Python transpiler contains research-grade optimization passes and weight compression algorithms, giving the project a hybrid character. The codebase is well-structured with clean abstractions for backends and operators, but some backends (WebGPU, WebAssembly) are partially complete and require specific build toolchains.

RATING: 7.3/10 MAINT: stale DRIFT: medium
#onnx-inference #web-ml #webgpu #webgl #wasm #neural-network #graph-optimization #deep-neural-networks #accelerate #optimization #javascript
▸ 4 reusable components found
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