The Web Neural Network API (WebNN) brings accelerated machine learning capabilities directly to web applications. With WebNN, developers can harness the power of neural networks within the browser environment, enabling a wide range of AI-driven use cases without relying on external servers or plugins. What is WebNN? WebNN is a JavaScript API that provides a high-level interface for executing neural network inference tasks efficiently on various hardware accelerators, such as CPUs, GPUs, and d...| Web Machine Learning
The Machine Learning for the Web Community Group has been launched. The mission of the Machine Learning for the Web Community Group (WebML CG) is to make Machine Learning a first-class web citizen by incubating and developing a dedicated low-level Web API for machine learning inference in the browser. Please see the charter for more information. The group invites browser engine developers, hardware vendors, web application developers, and the broader web community with interest in Machine Lea...| Web Machine Learning
The WebNN Polyfill has been published to NPM. It is a JavaScript implementation of the WebNN API, based on TensorFlow.js that supports multiple backends for both Web browsers and Node.js. With this polyfill, Web developers are able to experience the WebNN API early before the native implementations are shipped. Meanwhile, it can be treated as an independent implementation to help validate the feasibility and stability of the WebNN specification.| Web Machine Learning
🌱 This W3C Community Group started incubating work in 2018 for a possible Web Neural Network API, in response to encouraging feedback from a TPAC breakout session. Starting October 2018, this Community Group identified key use cases working with diverse participants including major browser vendors, key ML JS frameworks, interested hardware vendors, web developers, and started drafting the Web Neural Network API specification. 🚀 Following the two-year incubation period in this Community ...| Web Machine Learning
Introduction Machine Learning (ML) is a branch of Artificial Intelligence. A subfield of ML called Deep Learning with its various neural network architectures enables new compelling user experiences for web applications. Use cases range from improved video conferencing to accessibility-improving features, with potential improved privacy over cloud-based solutions. Enabling these use cases and more is the focus of the newly launched Web Machine Learning Working Group. Progress While some of th...| Web Machine Learning
WebNN-native is a native implementation of the Web Neural Network API. It provides several building blocks: WebNN C/C++ headers that applications and other building blocks use. The webnn.h that is an one-to-one mapping with the WebNN IDL. A C++ wrapper for the webnn.h Backend implementations that use platforms’ ML APIs: DirectML on Windows 10 OpenVINO on Windows 10 and Linux oneDNN on Windows 10 and Linux XNNPACK on Windows 10 and Linux Other backends are to be added| Web Machine Learning
In the WebNN API, the Operand objects represent input, output, and constant multi-dimensional arrays known as tensors. The NeuralNetworkContext defines a set of operations that facilitate the construction and execution of this computational graph. Such operations may be accelerated with dedicated hardware such as the GPUs, CPUs with extensions for deep learning, or dedicated ML accelerators. These operations defined by the WebNN API are required by models that address key application use case...| Web Machine Learning
A core abstraction behind popular neural networks is a computational graph, a directed graph with its nodes corresponding to operations (ops) and input variables. One node’s output value is the input to another node. The WebNN API brings this abstraction to the web. In the WebNN API, the Operand objects represent input, output, and constant multi-dimensional arrays known as tensors. The NeuralNetworkContext defines a set of operations that facilitate the construction and execution ...| Web Machine Learning
Today W3C Advisory Committee Representatives received a Proposal to review a draft charter for the Web Machine Learning Working Group. As part of ensuring that the community is aware of proposed work at W3C, this draft charter is public during the Advisory Committee review period. W3C invites public comments through 03:59 UTC on 2021-03-27 (23:59, Eastern time on 2021-03-26) on the proposed charter. Please send comments to public-new-work@w3.org, which has a public archive lists.w3.org/Archiv...| Web Machine Learning