How ONNX Runtime is Evolving AI in Microsoft with Intel
With the goal of bringing AI features to devices, the Microsoft Office team has been working with Intel and ONNX Runtime for over five years to integrate AI capabilities into their array of productivity products. The extension of AI inference deployment from servers to Windows PCs enhances responsiveness, preserves data locally to protect privacy, and increases the versatility of AI tooling by removing the requirement for an internet connection. These advancements keep powering Office features like neural grammar checker, ink form identification, and text prediction.
What is ONNX Runtime
As a result of their extensive involvement and more than two decades of cooperation, Intel and Microsoft are working more quickly to integrate AI features into Microsoft Office for Windows platforms. The ONNX Runtime, which enables machine learning models to scale across various hardware configurations and operating systems, is partially responsible for this accomplishment. The ONNX runtime is continuously refined by Microsoft, Intel, and the open-source community. When used in this way, it enhances the efficiency of Microsoft Office AI models running on Intel platforms.
AI Generative
With ONNX Runtime, you can incorporate the power of large language models (LLMs) and generative artificial intelligence (AI) into your apps and services. State-of-the-art models for image synthesis, text generation, and other tasks can be used regardless of the language you develop in or the platform you need to run on.
ONNX Runtime Web
With a standard implementation, ONNX Runtime Web enables cross-platform portability for JavaScript developers to execute and apply machine learning models in browsers. Due to the elimination of the need to install extra libraries and drivers, this can streamline the distribution process.
ONNX Runtime Java
Using the same API as cloud-based inferencing, ONNX Runtime Mobile runs models on mobile devices. Swift, Objective-C, Java, Kotlin, JavaScript, C, and C++ developers can integrate AI to Android, iOS, react-native, and MAUI/Xamarin applications by using their preferred mobile language and development environment.
ONNX Runtime Optimization
Inference models from various source frameworks (PyTorch, Hugging Face, TensorFlow) may be efficiently solved by ONNX Runtime on various hardware and software stacks. In addition to supporting APIs in many languages (including Python, C++, C#, C, Java, and more), ONNX Runtime Inference leverages hardware accelerators and functions with web browsers, cloud servers, and edge and mobile devices.
Ensuring optimal on-device AI user experience necessitates ongoing hardware and software optimization, coordinated by seasoned AI-versed experts. The most recent ONNX Runtime capabilities are regularly added to Microsoft Office’s AI engine, guaranteeing optimal performance and seamless AI model execution on client devices.
Intel and Microsoft Office have used quantization, an accuracy-preserving technique for optimizing individual AI models to employ smaller datatypes. “Microsoft Office’s partnership with Intel on numerous inference projects has achieved notable reductions in memory consumption, enhanced performance, and increased parallelization all while maintaining accuracy by continuing to focus on our customers,” stated Joshua Burkholder, Principal Software Engineer of Microsoft’s Office AI Platform.
With the help of Intel’s DL Boost, a collection of specialized hardware instruction sets, this method reduces the on-device memory footprint, which in turn reduces latency. The ONNX Runtime has been tuned to work with Intel’s hybrid CPU design, which combines efficiency and performance cores. With Intel Thread Director, this is further enhanced by utilising machine learning to schedule activities on the appropriate core, guaranteeing that they cooperate to maximise performance-per-watt.
Furthermore, on-device AI support for Office web-based experiences is being provided by Intel and Microsoft in partnership. The ONNX Runtime Web makes this feasible by enabling AI feature support directly in web applications, like Microsoft Designer.
Balancing Cloud and On-device
With the advent of AI PCs, particularly those featuring the latest Intel Core Ultra processor, more workloads are being able to move from cloud-based systems to client devices. Combining CPU , GPU , and NPU , Intel Core Ultra processors offer complementary AI compute capabilities that, when combined with model and software optimizations, can be leveraged to provide optimal user experience.
Even while the AI PC opens up new possibilities for executing AI activities on client devices, it is necessary to assess each model separately to ascertain whether or not running locally makes sense. AI computation may take on a hybrid form in the future, with a large number of models running on client devices and additional cloud computing used for more complicated tasks. In order to aid with this, Intel AI PC development collaborates with the Office team to determine which use cases are most appropriate for customers using the Intel Core Ultra processor.
The foundation of Intel and Microsoft’s continued cooperation is a common goal of an AI experience optimized to span cloud and on-device with products such as AI PC. Future Intel processor generations will enhance the availability of client compute for AI workloads. As a result, Intel may anticipate that essential tools like Microsoft Office will be created to provide an excellent user experience by utilizing the finest client and cloud technologies.
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