On Friday, December 19, 2025 at 11:50:19 AM UTC-8 Chromestatus wrote:

*Contact emails*
[email protected], [email protected], [email protected], 
[email protected]

*Explainer*
https://github.com/webmachinelearning/webnn/blob/main/explainer.md

*Specification*
https://webmachinelearning.github.io/webnn 

*Design docs*

https://docs.google.com/document/d/1kIc5NO1PU1DbGHKOsnan-
tEhV01L6_K3OLA5g0o4Ts4/edit?resourcekey=0-ltUDiDZqxKgARhtNqsGJeg

*Summary*
WebNN seeks to enable web applications and frameworks to take advantage of 
native operating system services for machine learning and the underlying 
hardware capabilities available on a user's computer to implement 
consistent, efficient, and reliable ML experiences on the web. 

*Blink component*
Blink>WebML 
<https://issues.chromium.org/issues?q=customfield1222907:%22Blink%3EWebML%22>

*Web Feature ID*
webnn <https://webstatus.dev/features/webnn> 

*TAG review*
https://github.com/w3ctag/design-reviews/issues/933 

*TAG review status*
Issues addressed

*Origin Trial documentation link*
https://webnn.io/en/learn/get-started/quickstart 

*Risks*


*Interoperability and Compatibility*
Aside from the risk that other browsers do not implement (which is trending 
towards unlikely given that Edge is planning to ship alongside Chrome and 
Firefox is planning to implement) the primary interoperability risk is that 
a WebNN implementation needs to abstract over the ML acceleration 
capabilities of the underlying platform and hardware. The specification 
mitigates this risk by defining a set of ML operations which must be 
supported and provides a feature detection mechanism for discovering 
additional operations that may be optionally supported. This allows 
developers to write broadly interoperable code by using only the required 
operations or design their applications to adapt to additional capabilities 
at runtime. 

*Gecko*: Positive (https://github.com/mozilla/standards-positions/
issues/1215#issuecomment-3520278819)

*WebKit*: No signal (https://github.com/WebKit/standards-positions/
issues/486) Apple is participating in the WebML WG but has made no public 
statements about implementation.

*Web developers*: Positive Microsoft's ONNX runtime includes an execution 
provider for WebNN (https://onnxruntime.ai/docs/tutorials/web/ep-webnn.html). 
Google's LiteRT.js framework has announced that they are working on WebNN 
support (https://youtu.be/HAjotVloAvI?t=219).

*Other signals*: The WebNN implementation in Chromium is a collaboration 
between Google, Intel, and Microsoft. Edge will be launching WebNN at the 
same time as Chrome.

*Ergonomics*
For some use cases, particularly real-time ML processing of video, 
integration between WebNN and WebGPU is required. Support for this has been 
prototyped on some platforms as an `exportToGPU()` method that takes an 
`MLTensor` and makes it available as a `GPUBuffer` but this is not yet 
available on all platforms.

*Activation*
Most developers don't write JavaScript or WebGPU shader code to execute 
their ML models by hand. Instead frameworks such as LiteRT.js, ONNX Runtime 
Web, WebLLM or Transformers.js are used. Support for WebNN in these 
frameworks (already starting to appear) will be a large factor in the 
adoption of this API.

*Security*
The primary security risk is exposing the underlying platform frameworks to 
potentially malicious content. This is mitigated through sandboxing of the 
GPU process and fuzz testing. The risks are similar to WebGPU, though the 
expressiveness of WebNN is lower than WGSL.

*WebView application risks*

Does this intent deprecate or change behavior of existing APIs, such that 
it has potentially high risk for Android WebView-based applications? 
This is a new API. 


*Goals for experimentation*
*No information provided*


Due to GoogleChrome/chromium-dashboard#4155 
<https://github.com/GoogleChrome/chromium-dashboard/issues/4155> this 
wasn't filled in. It should read:

The goal of this experiment is to understand how well real-world models 
abstract to the operations supported by WebNN and how well WebNN can map 
these operations to those supported by the hardware of real-world users.
 

*Ongoing technical constraints*
WebNN can currently execute models on CPU across all platforms but support 
for GPU and NPU execution is more limited. This will expand as development 
continues.

*Debuggability*
There is currently no special DevTools support for this API beyond regular 
JavaScript debugging. A feature we have considered (and prototyped via an 
internal extension and the --webnn-coreml-dump-model, 
--webnn-ort-dump-model, and --webnn-tflite-dump-model browser flags) is 
inspecting graphs constructed by a site and exporting them in the 
underlying platform format so that they can be inspected with other tools. 

*Will this feature be supported on all six Blink platforms (Windows, Mac, 
Linux, ChromeOS, Android, and Android WebView)?*
Yes

*Is this feature fully tested by web-platform-tests 
<https://chromium.googlesource.com/chromium/src/+/main/docs/testing/web_platform_tests.md>?*
Yes 
https://wpt.fyi/results/webnn?label=master&label=
experimental&aligned&q=webnn

*DevTrial instructions*
https://webnn.io/en/learn/get-started/installation

*Flag name on about://flags*
web-machine-learning-neural-network 

*Finch feature name*
WebMachineLearningNeuralNetwork 

*Requires code in //chrome?*
True

*Tracking bug*
https://issues.chromium.org/issues/40206287

*Estimated milestones*
Origin trial desktop first145 Origin trial desktop last147 DevTrial on 
desktop125 Origin trial Android first145 Origin trial Android last147 DevTrial 
on Android125 Origin trial WebView first145 Origin trial WebView last147 

*Link to entry on the Chrome Platform Status*
https://chromestatus.com/feature/5176273954144256?gate=5186064701194240

*Links to previous Intent discussions*
Intent to Prototype: https://groups.google.com/a/chromium.org/g/blink-dev/c/
PD6TDMDS9mg


This intent message was generated by Chrome Platform Status 
<https://chromestatus.com>. 

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