Spec mentors for WebNN

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Corentin Wallez

Nov 5, 2021, 7:07:47 AM11/5/21
to spec-m...@chromium.org, ningxin hu
Hey mentors,

Ningxin (CCed) is looking for mentors for the WebNN specification and the Blink Intent process to start prototyping it in Chrome. When he previously sent an email to spec-m...@chromium.org it bounced, so I'm sending this one, hoping I'll have the correct access.

Let me know if spec mentors aren't the correct group for this and we'll figure out something else.



Jeffrey Yasskin

Nov 5, 2021, 10:30:14 AM11/5/21
to Corentin Wallez, spec-m...@chromium.org, ningxin hu
I can take this, since I also took WebML. 

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Corentin Wallez

Nov 5, 2021, 10:33:58 AM11/5/21
to Jeffrey Yasskin, spec-m...@chromium.org, ningxin hu
Great, thank you!

Jeffrey Yasskin

Nov 9, 2021, 8:16:10 PM11/9/21
to Hu, Ningxin, Corentin Wallez, Jeffrey Yasskin, Kostiainen, Anssi, Chai Chaoweeraprasit, Rafael Cintron

This looks solid. I think you've captured the main points in https://tag.w3.org/explainers/.

The one thing I'm missing is some mention of the harm that biased neural nets have caused, with ways for developers to avoid reproducing that harm. I filed https://github.com/webmachinelearning/webnn/issues/225 to ask the WG to add that to the specification. I don't think it needs to block the I2P, but you might get other questions along those lines as you expand your audience.

Given the apparent cross-browser support in the CG and the workshop, now might be a good time to ask for Mozilla and Webkit signals, using the instructions in https://bit.ly/blink-signals. You only need those signals by the Intent 2 Ship phase, so we often wait for the origin trial to ask, but if they already support the work, may as well get that recorded now.

Would you like to get a more specific crbug component?


On Fri, Nov 5, 2021 at 8:22 AM Hu, Ningxin <ningx...@intel.com> wrote:

Awesome, thanks Jeffrey and Corentin. CC+ Anssi (W3C WebML WG Chair), Chai (WebNN co-editor), Rafael (Edge).


Jeffrey, I am pasting the Intent to Prototype WebNN draft below. Please help review.


Contact emails






Design docs



Construct and execute computational graphs that are at the heart of neural networks, taking advantage of on-device hardware acceleration. Currently, JavaScript frameworks must call WebGL, WebGPU and WebAssembly for the individual compute operations when executing computational graphs. The WebNN API allows JavaScript frameworks to access the native operating system machine learning APIs and the hardware accelerators underneath for the best performance and reliability of results.

Blink component



Deep Learning, a subfield of Machine Learning (ML), with its various neural network architectures enables new user experiences for web applications that range from improved video conferencing to accessibility-improving features, with potential for improved privacy over cloud-based solutions. Key neural network-based use cases that apply to a wide range of web applications include e.g., face detection, semantic segmentation, style transfer, super resolution, image captioning, machine translation, and noise suppression. While some of these use cases can be implemented in a constrained manner with existing Web APIs (e.g. WebGL, WebGPU and WebAssembly), the lack of access to platform capabilities beneficial for ML such as dedicated ML hardware accelerators constraints the scope of experiences and leads to inefficient implementations on modern hardware. This disadvantages the web platform in comparison to native platforms.

Initial public proposal

TAG review


TAG review status

Issues addressed


Interoperability and Compatibility

All major browser vendors participated in the Community Group [1] that successfully completed the incubation of this API and graduated it to the Working Group [2] for standardization. A well attended W3C workshop [3] that brought together browser vendors and machine learning practitioners reviewed the API and recommended it for standardization. Public support from Edge and Microsoft [4] with key contributors involved in the work. Significant interest and positive feedback from machine learning JS frameworks in using WebNN when it is available, see e.g. TensorFlow.js/TensorFlow-Lite Web [5] and OpenCV.js [6]. An interoperability risk is to define the core operators that can be implemented cross-platforms and support the breadth of ML JS frameworks, however the members of the working group have experience defining and testing such interoperable machine learning operator set.


[1]: https://www.w3.org/groups/cg/webmachinelearning

[2]: https://www.w3.org/groups/wg/webmachinelearning

[3]: https://www.w3.org/2020/06/machine-learning-workshop/

[4]: https://www.w3.org/blog/2021/04/w3c-launches-the-web-machine-learning-working-group/

[5]: https://github.com/tensorflow/sig-tfjs/pull/2

[6]: https://github.com/opencv/opencv/pull/20406

Gecko: No signal

WebKit: No signal

Web developers: Positive (https://github.com/tensorflow/sig-tfjs/pull/2) The RFC of WebNN Delegate for TensorFlow Lite Web got good feedback and approval.

Other signals:


webnn-polyfill: https://github.com/webmachinelearning/webnn-polyfill


Warnings and errors are exposed via dev tools. Specialized tools for debugging are TBD.

Is this feature fully tested by web-platform-tests?

Not yet. Working in progress, e.g., adding IDL test [1]


[1] https://github.com/web-platform-tests/wpt/pull/31201

Flag name


Requires code in //chrome?


Estimated milestones

No milestones specified

Link to entry on the Chrome Platform Status


This intent message was generated by Chrome Platform Status.

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