W3C Workshop on Web and Machine Learning

🌱 A virtual event with pre-recorded talks and interactive sessions

W3C Workshop Report on Web and Machine Learning

Executive Summary

W3C organized a workshop on Web and Machine Learning over the course of August and September 2020. This workshop brought together web platform and machine learning practitioners to enrich the Open Web Platform with better foundations for machine learning.

This first-ever virtual W3C workshop kicked off with the publication of 34 talks in August 2020 discussing opportunities and challenges of browser-based machine learning, web platform foundations, as well as developer’s, and user’s perspectives into machine learning technologies in the context of the web platform.

These perspectives brought up by top experts from the field were carefully evaluated and discussed across 25 online workstreams inviting input from 200+ workshop participants during August and September. The workshop culminated into a series of four live sessions held in September 2020 that synthesized findings from the workstreams and concluded the workshop with proposed next steps for standardization and incubation.

The workshop participants identified as a prime standardization opportunity the low-level primitives for machine learning inference exposed through the Web Neural Network API. This work has been incubated at a W3C Community Group since late 2018. Following the workshop, as a concrete next step, the W3C sent an advance notice of the development of a charter for a Web Machine Learning Working Group to standardize the Web Neural Network API. Furthermore, multiple opportunities for closer collaboration between the W3C and Ecma were identified to accelerate development of the JavaScript language features beneficial for machine learning. Additional proposals for incubation were identified, including Model Loader API to explore interoperability of an API for loading pre-trained machine learning models, media pipeline optimizations, and machine-readable Model Cards proposal to help address machine learning bias and transparency issues. The proposals will be further developed in W3C Community Groups.

The virtual workshop format was well-received and demonstrated the W3C’s ability to adapt to the changing environment that relies on online collaboration technologies in the midst of the global pandemic.


Opportunities and Challenges of Browser-Based Machine Learning

The first live session started by articulating the workshop live sessions around four top-level topics: Opportunities and Challenges, Web Platform Foundations, Developer’s Perspective, and User’s Perspective. Throughout the workshop, these top-level signposts guided discussions toward insights that help make progress against the following goals:

The focus of the discussions of the first session were around opportunities and challenges with a specific view on unique opportunities of browser-based machine learning, and identifying obstacles hindering adoption of machine learning technologies on the web platform.

Improving existing web platform capabilities

The fitness of the WebGPU API to support machine learning frameworks sparked active discussion and identified new WebGPU extensions that could substantially benefit ML - most notably the proposed subgroup operations extension. This feature promises to speed up algorithms that need to specialize general graphs such as neural network computational graphs, a core concept of the emerging Web Neural Network API.

This raised the question of whether it is important to solve the interoperability at the hardware level, or whether it would be possible to meet users' needs with higher level constructs. Multi-Level IR Compiler Framework (MLIR), a common intermediate representation, that also supports hardware specific operations, is being explored in parallel and might offer an MLIR dialect that could become a portability layer to help with the explosion of the number of operations. MLIR project continues its work in parallel with close coordination with the W3C machine learning efforts thanks to overlapping participation.

The lack of float16 in JavaScript and WebAssembly environments was discussed as an issue in particular for quantized models where it leads to higher memory usage and lower inference speed. The lack of this data type was linked mostly to lack of momentum in its standardization process. The workshop participants recommended providing Ecma TC39 with input to help expedite and prioritize float16 support from its current early stage status. For WebAssembly, the WASI-NN initiative, which proposes to standardize the neural network system interface for WebAssembly programs, is considering adding support for float16 and int8 buffers, with a way to emulate those as needed.

The discussion on memory copies highlighted the inefficiencies in machine learning web apps within the browser media pipeline. This usage scenario triggers many more memory copies compared with corresponding native applications, hindering performance of web-equivalents. Modern in-browser media pipeline can make use of WebTransport and WebCodecs, as well as more established primitives such as WHATWG Streams, transferable streams, and SharedArrayBuffer, but these APIs alone do not avoid memory copies. Given the complexity of this space, the discussions converged on picking a few key usage scenarios where redundant or excessive memory copies are problematic and proposed optimizing those paths. One such key scenario involves video feed, from capture to render, or audio input in a similar fashion. A separate TPAC breakout session is organized to deep dive on the memory copies issue.

The discussion on a permission model for Machine Learning APIs emerged from potential privacy implications of these APIs as well as the substantial power requirements that these APIs may surface and their impact on user experience. Machine learning APIs are similar in that regard to WebGL, WebGPU and WebAssembly; the discussions thus suggested the W3C Technical Architecture Group would be well positioned to drive coordination on the larger question of permission model for compute-heavy APIs from a platform-wide perspective.

Extending beyond the browser

Several contributions to the workshop noted the applicability to non-browser JS environments of the browser-targeted machine learning capabilities, in particular Node.js. The talk Extending W3C ML Work to Embedded Systems drawing from earlier experiences, suggested avoiding an anti-pattern of defining “light” versions of Web APIs for constrained environments. Given Ecma TC53 defines standard JavaScript APIs for low level device operations — digital, serial, network sockets, for constrained devices — it was proposed to strengthen coordination between TC53 and related W3C groups working on ML APIs. The goal of this coordination would be to ensure a common conceptual basis while not necessarily targeting a one-size-fits-all API design across classes of devices.

Web Platform Foundations for Machine Learning

The second live session focused on Web Platform Foundations with a goal to understand how machine learning fits into the Web technology stack.

Considerations for creating and deploying models

This live session discussed machine learning model formats, use cases and requirements for protecting machine learning models, in-browser training drawing from early experiments in this space, and opportunities for leveraging training across devices in the web context.

The lack of a standard format for packaging and shipping machine learning models was flagged as an issue in many of the lightning talks from experts in Web Neural Network API, TensorFlow.js, ONNX, DirectML, and MLIR, among others. This space is also not ready for standardization given the ongoing rapid innovation and evolution in model formats. Instead, the emerging approach is to initially focus on defining a Web API for accelerating established reusable ML model building blocks i.e. operations. In many commonplace models, such as in popular computer vision models, over 90% of compute time is typically spent on a small set of compute-intensive operations. As a result, scoping the proposed Web API to hardware-accelerate these compute-intensive operations was deemed the most pragmatic approach to unlock new machine learning-assisted user experiences on the Web in the near term.

Several workshop contributors relayed the need to protect machine learning models as some ML model providers need to ensure their models cannot be extracted from a web application running within the browser due to IPR concerns. The workshop discussions noted similar content protection issues in other fields with some solutions deployed in production (Encrypted Media Extensions for video content)  while others remain unresolved - e.g. protection of 3D assets in web-based games as discussed at the W3C Workshop on Web Games held June 2019. These efforts have shown that this is not an easy-to-solve problem in the web context, and that solutions risk creating restrictions on security and privacy researchers’ ability to check for vulnerabilities. Further research is needed to understand what trade-offs are possible in the specific case of ML models.

The workshop participants also discussed the nascent area of in-browser training. Most Machine Learning in-browser efforts are focused on inference rather than training, with a few exceptions. The Teachable Machine project enables in-browser transfer learning and identified concrete issues to improve the user experience of ML training. One issue was the inability to operate the training process in a a background tab. The System Wake Lock API might provide a solution for this and a corresponding use case was submitted for consideration. An additional outcome of this discussion was a recommendation to document successful real-world usages (e.g. Teachable Machine) with transfer learning as the most likely initial training use case for related browser API work. Web Neural Network API as a graph-based API provides extension points to cater for transfer learning in its future version.

Training across devices discussion solicited insights to help inform the role of edge computing in machine learning training and interactions with the web platform. This topic was discussed in the talk on Collaborative Learning that identified federated learning, distributed learning, and reinforcement learning in the web context as gaps. The talk Enabling Distributed DNNs for the Mobile Web Over Cloud, Edge and End Devices proposed a partition-offloading approach to leverage computing resources of the end devices and the edge server. The conclusion from discussions was to work with the Web & Networks Interest Group to ensure its edge computing workstream considers ML usages.

Machine Learning Experiences on the Web: A Developer's Perspective

The third live session focused on topics that offer the developer's perspective and discussed learnings from authoring ML experiences, reusing pre-trained ML models, discussing technical solutions and gaps.

Applying web design principles to ML

Progressive enhancement and graceful degradation are established web development and design strategies — the workshop participants discussed how they would apply in the context of machine learning. More specifically, how to bring more ML features as optional improvements on more powerful devices and browsers without breaking web compatibility? A goal of the discussion was to identify mechanisms and issues in designing ML APIs that enable these patterns to be used, giving developers visibility on how well a given experience will work on the end-user's device. One approach was to start with models that provide a reduced version with acceptable performance. Conversely, there will always be cases where developers will want to target only hardware-accelerated devices and won't support lower end devices for performance critical use cases such as AR or VR experiences with live video feed processing. A key part to enabling this is model introspection to understand model performance characteristics, although it was noted to be hard to provide reliably. Different level of fallback mechanisms were discussed, from the fine-grained operation-level to the entire model level with different models swapped in depending on implementation, platform or hardware capabilities. Conversely, scaling up a model requires a lot of introspection capabilities.

A key goal of W3C standardization is to ensure interoperable implementations which raised questions about conformance testing for ML APIs. A learning from WebGPU was that a computation can have varying hardware implementations and thus varying precision: in that situation, numerical precision is independent of the browser's WebGPU implementation, and testing end-to-end operations is less tractable. WebGPU has parallels with the WebNN API, and a proposal emerged that a conformance test suite for ML APIs need to consider multiple levels of conformance, both operator-level and model-level interoperability.

Improving web developer ergonomics

Machine learning processing typically involves a lot of matrix operations, and thus the ergonomics of the JavaScript language for these operations plays a direct role in the ergonomics of JS APIs. JavaScript operator overloading would improve the ergonomics of training and helps with custom operations, a proposal being discussed in the context of Web Neural Network API. Operator overloading is a Stage 1 proposal at Ecma TC39 roughly corresponding to the W3C incubation phase, and the workshop participants supported a proposal to feed TC39 with ML use cases to help TC39 prioritize this feature accordingly.

ML frameworks that today rely on WebGL API to enable fast parallelized floating point computation accelerated by GPUs surfaced an issue with WebGL garbage collection (cf. Opportunities and Challenges for TensorFlow.js and beyond, Fast client-side ML with TensorFlow.js). The side effects of WebGL garbage collection cause unpredictable performance, and given JS provides automatic garbage collection, some web developers expect GC to also happen similarly when interacting with APIs that interface with hardware. However, in the browser context, WebGL memory is not automatically garbage collected, nor can it reliably be. JS libraries such as TensorFlow.js have resorted to exposing an API for the WebGL backend to explicitly manage memory. A suggestion was made that the Web Neural Network API should consider adding a similar mechanism to allow resources to be freed eagerly. JavaScript engines have also room to optimize their approaches to garbage collection as the Chakra engine previously demonstrated in EdgeHTML.

Neural network-oriented graph database discussion reviewed known model storage issues on the client, and evaluated the feasibility of a neural network-oriented graph database for the web. Pipcook, a front-end oriented DL framework talk highlighted that a deep learning model is essentially a graph with weights and suggested that by providing a graph-orientated database that stores the information in a graph format we could reduce the serialization overhead. Existing web platform storage APIs such as IndexedDB are not optimized for storing graph data. The group identified the Filesystem Access API in active incubation as one possible alternative for IndexedDB that could help alleviate the storage issues.

Machine Learning Experiences on the Web: A User's Perspective

The first half of the last live session focused on user's perspective topics under the banner of Web & ML for all to highlight the profound impact these technologies combined will have on billions of people who are using the Web.

Web & ML for all

Discussions on bias and model transparency highlighted the impact on minorities and underrepresented groups as explored in the talks We Count: Fair Treatment, Disability and Machine Learning and Bias & Garbage In, Bias & Garbage Out. Among the practical mitigations discussed, the most promising idea involved a browser-assisted mechanism to find out about the limitations and performance characteristics of ML models used in a Web app. This would build on an approach published in Model Cards for Model Reporting where making this report machine-discoverable would allow for the web browser to offer a more integrated user experience.

The privacy issues in speech recognition highlighted the challenges with standardizing the Web Speech API. As the talk Getting the Browser to Recognize Speech highlighted, this includes issues around fingerprinting and issues arising from client-side or server-side implementation strategies for the speech recognition engine. End users are not currently made aware of whether their speech data is sent to and stored on the server, or whether all the processing remains within the client. It was suggested the Web Speech API spec could mandate client-side only recognition, or at least allowing users to require it for enhanced privacy. Such a proposal needs a champion to bring the Web Speech API to standardization to reinvigorate work in this space.

Privacy is an integral part of the web platform, so it is critical that ML APIs are designed to be privacy-preserving, and its stakeholders have a shared responsibility to our global user base to balance privacy with new user experiences enabled by new capabilities being brought to the platform. An important aspect of developing the web platform is to ensure a tight feedback loop and productive joint effort between domain- and privacy-experts. To that end, a concrete next step recommended by the workshop participants was to organize an early privacy review of the Web Neural Network API.

Building an extensible web platform for ML, one abstraction at a time asked explicitly whether the workshop participants are in agreement that advancing with standardization of low-level capabilities e.g. WebNN API is the pragmatic first step. Discussion that followed unanimously concluded the WebNN API provides the right level of granularity to make ML efficient today. While a lot of work beyond the WebNN API is needed, the participants clearly agreed that WebNN API is a critical first piece, and in a good enough shape to transition to standardization.

Next Steps

The second half of the fourth session was spent discussing overall workshop conclusions and next steps to chart the path forward.

Next Steps in Standardization

Building on the emerging consensus that the WebNN API is the right first step in bringing Machine Learning capabilities to the Web platform, the workshop participants propose that a new W3C Working Group should be formed to start work on standardizing it. To that end, the W3C sent an advance notice following the workshop that a Web Machine Learning Working Group Charter is work in progress.

W3C is also encouraged to liaise with Ecma TC39 with regard to the value of standardizing float16 and operator overloading in the context of Machine Learning processing.

Next Steps in Incubation

The workshop participants supported the continued incubation of the Model Loader API in the Web Machine Learning Community Group to validate its ability to support interoperable deployment of ML models across OSes and devices.

The said Community Group recently opened a new repository to track early proposals, where the idea of formalizing support for ML Report Cards will be submitted.

The need of a coordinated approach to dealing with gating access to compute-intensive APIs and to optimizing memory copy in media processing is proposed to be taken up respectively by the W3C Technical architecture and the Media & Entertainment Interest Group.

Other exploratory work

Some questions remain more open-ended and workshop participants and the wider community are invited to continue exploring their problem space to identify if and how they can be answered in the context of expanding the Web platform capabilities to the Machine Learning ecosystem:


The organizers express deep gratitude to those who helped with the organization and execution of the workshop, starting with the members of the Program Committee who provided initial support and helped shape the workshop.

The Chairs of the workshop, Anssi Kostiainen and Kelly David, provided the leadership and vision to build a community and drive many conversations forward in the unusual context of the first fully virtual W3C workshop.

Thanks to Futurice for sponsoring the event, which among other things allowed all the recorded presentations to be captioned and enabled live interpretation of the live sessions in Chinese.

Thank you to Marie-Claire Forgue for her support in post-processing the pre-recorded talks  and all W3C team members who helped with organizing the workshop.

And finally, a big thank you to the speakers who helped seed many critical discussions for the future of Machine Learning on the Web, and to participants who helped develop a shared perspective on that future.