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CfC: adopt WebNN API and publish as a First Public Working Draft; respond by 17 Jun 2021

From: Kostiainen, Anssi <anssi.kostiainen@intel.com>
Date: Thu, 10 Jun 2021 16:16:42 +0000
To: "public-webmachinelearning-wg@w3.org" <public-webmachinelearning-wg@w3.org>
Message-ID: <C42F6036-6F94-4D47-8EFD-CCBDB730C06A@intel.com>
Hi Web Machine Learning Working Group,

This is a Call for Consensus (CfC) to adopt the Web Neural Network API (WebNN API) [1] from the Web Machine Learning Community Group (CG) into this Working Group and publish it as a First Public Working Draft (FPWD) as per the WG Charter [2].

This specification has been incubated in the Community Group and has received initial security, privacy and architecture reviews. The CG has also developed and maintains a polyfill and a native implementation that evolve together with the specification. Considering these signals, we believe the WebNN API meets the expectations of a First Public Working Draft and is ready to start its journey on the W3C Recommendation Track.

It should be noted, a specification published as a FPWD is expected to change as the work progresses and the group receives further feedback from implementers, ML frameworks, web developers, through horizontal reviews and other groups working in this space.

As this is the first Call for Consensus we're running in this group, some background on what a call for consensus is: the W3C Process requires chairs to assess the level of consensus before taking some of the group's formal decisions. To enable as broad input from Working Group participants as possible, including those that may not be in a position to join synchronous discussions during teleconferences, we will in general assess this consensus by sending these Call for Consensus messages (sometimes abbreviated as CfC) to the Working Group's mailing list, and collect feedback for a defined duration (usually between 7 and 10 days).

Please indicate any objections or concerns on this list before EOB 17 June 2021. Silence is considered consent.

Thanks,

-Anssi (WebML WG Chair)

[1] https://webmachinelearning.github.io/webnn/
[2] https://www.w3.org/2021/04/web-machine-learning-charter.html
Received on Thursday, 10 June 2021 16:19:33 UTC

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