Motivation and Background
+
+ Computer Vision enables computers to gain understanding from images
+ or videos, Natural Language Processing enables interaction between
+ computers and human languages, and Speech Recognition enables
+ computers to recognize and translate spoken language into text.
+ Bringing these experiences to the web in a privacy-preserving manner
+ requires efficient machine learning inference capabilities built into
+ the browser.
+
Enabling Machine Learning inference in
the browser (as opposed e.g. to in the cloud) enhances privacy, since input
data such as locally sourced images or video streams stay within the
@@ -200,20 +208,26 @@
This Web API for neural network inference hardware acceleration:
- - Allow to construct a neural network computational graph by common
- building blocks, including constant values and base operations such
- as convolution, pooling, softmax, normalization, fully connected,
- activation, recurrent neural network (RNN) and long short-term memory
- (LSTM);
+
- Allows to construct a neural network computational graph by common
+ building blocks required by well-known model architectures: constant
+ values and base operations such as convolution, pooling, softmax,
+ normalization, fully connected, and activation;
- - Allow to compile the neural network to native optimized format
+
- Allows to compile the neural network to native optimized format
for hardware execution;
- - Allow to setup input from various sources on the Web, e.g. array
+
- Allows to setup input from various sources on the Web, e.g. array
buffers, media streams, schedule the asynchronous hardware execution,
and retrieve the output when hardware execution completes.
+
+ This Working Group puts priority on building blocks required by
+ well-known model architectures such as recurrent neural network
+ (RNN), long short-term memory (LSTM) and transformers in the fields
+ of Computer Vision, Natural Language Processing and Speech
+ Recognition.
+
The APIs in scope of this group will not be tied to any particular
platform and will be implementable on top of existing major platform
@@ -221,6 +235,11 @@
macOS/iOS Metal Performance Shaders and Basic Neural Network
Subroutines.
+
+ For each high-level building block that decomposes into well-known
+ lower-level operations, the APIs will informatively define a generic
+ emulation path to allow for future extensibility.
+
It may also work on a higher-level API to load a custom pre-trained Machine Learning model for inference in the browser.
@@ -237,11 +256,14 @@
algorithms.
- To avoid overlap with existing work, generic primitives used by
- traditional machine learning algorithms such as base linear algebra
- operations are out of scope. The WebGL and WebGPU shaders and
- WebAssembly SIMD are expected to address these requirements, see
- the Coordination section for details.
+ To avoid overlap with existing work, alignment with the Basic
+ Linear Algebra Subprograms (BLAS) interface is out of scope. The
+ WebGPU shaders and WebAssembly SIMD are expected to address the
+ BLAS compatibility requirement, see the Coordination section for
+ details.
+
+
+ Interoperability between the WebNN and WebGL APIs is out of scope.
@@ -253,14 +275,7 @@
More detailed milestones and updated publication schedules are
available on the group publication
- status page.
-
-
- Draft state indicates the state of the deliverable at the time
- of the charter approval. Expected completion indicates when
- the deliverable is projected to become a Recommendation, or otherwise
- reach a stable state.
+ "https://www.w3.org/groups/wg/webmachinelearning">group home page.
@@ -272,8 +287,7 @@
-
- Web Neural
- Network API
+ Web Neural Network API
-
@@ -281,12 +295,23 @@
inference that can take advantage of hardware acceleration.
- Draft state: Adopted from Web
- Machine Learning Community Group
+ Draft state: Working Draft
+
+
+ Adopted Draft: The title, stable URL,
+ and publication date of the
+ Adopted Draft which will serve as the basis for
+ work on the deliverable.
+
+
+ Exclusion Draft: The title, stable
+ URL, and publication date of the most recent
+ Exclusion Draft.
- Expected completion: [CR Q1 2022]
+ Expected completion: Q1 2025
@@ -315,7 +340,8 @@
Draft state: Explainer
+ "https://webmachinelearning.github.io/model-loader/">Adopted from Web
+ Machine Learning Community Group
Expected completion: [N/A]
@@ -330,7 +356,14 @@
Other Deliverables
-
The Working Group will develop a Working Group Note documenting ethical issues associated with using Machine Learning on the Web, to help identify what mitigations its normative specifications should take into account.
+
+ The Working Group develops Ethical
+ Principles for Web Machine Learning Working Group Note
+ documenting ethical issues associated with using Machine Learning
+ on the Web, to help identify what mitigations its normative
+ specifications should take into account.
+
Other non-normative documents may be created such as:
@@ -344,19 +377,6 @@
-
-
- Timeline
-
-
- - Q2 2021: First teleconference
-
- - Q2 2021: FPWD for Web Neural Network API
-
- - Q2 2022: CR for Web Neural Network API
-
-
-