Unleashing the Power of Windows AI Q&A with Qualcomm Technologies

Table of Contents:

1. Introduction
2. Qualcomm AI Software Team and AI Stack
   • Partnering with Microsoft
   • Bringing Qualcomm AI stack to Windows
   • Using Qualcomm AI stack on Snapdragon devices
3. Working with Onyx Runtime
   • Consistent solution for inference of AI models
   • Supporting Onyx runtime in partnership with Microsoft
   • Benefits of using Onyx runtime
   • Deployment of AI models with Qualcomm Neural Processing Engine SDK
4. Running Stable Diffusion Model on Qualcomm Snapdragon
   • Challenges and techniques for running Stable Diffusion Model
   • Live demo of stable diffusion model on Windows on Snapdragon device
5. Future Investments in AI by Qualcomm
   • Emerging use cases and markets for AI
   • Productivity, creativity, connectivity, and industrial applications
   • Technological advancements to support future AI models
6. Call to Action for Developers
   • Onyx runtime as a powerful and common stack for AI models
   • Visit Qualcomm booth and attend AI-related Sessions
   • Access Qualcomm AI Studio and other resources for development
7. The Ubiquity of AI Accelerators in Hardware
   • Availability of AI accelerators in Current hardware
   • Importance of AI accelerators for users and developers
8. Backward Compatibility and Support for Legacy Silicon
   • Ensuring compatibility across different hardware generations
   • Continuous improvements in the AI stack and APIs
9. Q&A Session
   • Further improvements to Snappy SDK and tools
   • Next steps for native experience with Onyx runtime
   • Moving towards 10 billion parameters on edge
   • Techniques for successful deployment and quantization
   • Forecast for AI accelerator ubiquity and user experience
10. Conclusion

Introduction

AI and machine learning have become essential components in various industries, providing solutions for productivity, creativity, connectivity, and more. As technology continues to advance, developers and organizations are constantly looking for ways to harness the power of AI on different platforms. In this article, we will explore how Qualcomm, in partnership with Microsoft, is enabling developers to run AI models on the edge using their AI stack and Onyx runtime. We will also discuss the challenges in deploying AI models, the support provided by Qualcomm's Neural Processing Engine SDK, and their vision for the future of AI investments. So let's dive in and discover how Qualcomm is revolutionizing AI on the edge.

1. Qualcomm AI Software Team and AI Stack

The Qualcomm AI software team is at the forefront of developing and optimizing AI software solutions. Working closely with Microsoft, they have brought the Qualcomm AI stack to Windows, enabling developers to leverage AI capabilities on a variety of devices. This collaboration has resulted in a range of exciting announcements related to bringing the Qualcomm AI stack to Windows.

The Qualcomm AI stack has been designed to provide a consistent and powerful ecosystem for running AI models on Snapdragon devices. By partnering with Microsoft, Qualcomm aims to deliver a seamless experience for developers, ensuring that their AI models can be easily deployed and executed on a wide range of Windows devices, including Windows on Snapdragon devices.

The Qualcomm AI stack has been extensively used on Android devices and other platforms, and now it has been optimized for Windows on Snapdragon devices. Developers can utilize the same stack across different platforms, making it easier to develop and deploy AI models in multiple environments. This means that developers can build a model once and use it across various devices, including cloud, edge, automotive, and more.

This collaboration between Qualcomm and Microsoft opens up new possibilities for developers who want to take AdVantage of the power of AI at the edge. With the Qualcomm AI stack, developers can Create innovative AI applications that run seamlessly on Snapdragon devices, delivering exceptional performance and efficiency.

2. Working with Onyx Runtime

One of the key aspects of running AI models on the edge is the adoption of Onyx runtime, a consistent solution for inference of AI models. Qualcomm has worked closely with Microsoft to support and integrate Onyx runtime into the Qualcomm AI stack, providing developers with a powerful and versatile runtime environment.

Onyx runtime offers a comprehensive set of tools and features for deploying AI models on a wide range of platforms, including Snapdragon devices. It provides a common framework that simplifies the deployment and execution process, ensuring compatibility and interoperability across different hardware architectures and operating systems.

By adopting Onyx runtime, developers can leverage a unified runtime environment for their AI models, regardless of the specific platform or hardware they are targeting. This allows for easier portability of models and enables developers to focus on building innovative AI applications without worrying about compatibility issues.

With Onyx runtime, developers can take advantage of a wide range of deployment options, including deploying models on device, leveraging cloud-Based services, and utilizing edge computing capabilities. This flexibility enables developers to optimize their AI models for different scenarios, ensuring efficient and reliable performance across various use cases.

To support the adoption of Onyx runtime, Qualcomm has collaborated with Microsoft to provide demos, documentation, and resources for developers. These initiatives aim to educate and empower developers to make the most out of Onyx runtime and unlock the full potential of AI on Snapdragon devices.

3. Running Stable Diffusion Model on Qualcomm Snapdragon

As part of their commitment to showcasing the capabilities of Snapdragon devices and the Qualcomm AI stack, Qualcomm has developed a stable diffusion model, which is a Generative AI model. This model provides a practical example of running AI models on the edge and demonstrates the immense potential of using AI to enhance various applications.

A live demo of the stable diffusion model on a Windows on Snapdragon device showcases the power and performance of the Qualcomm AI stack. The demo allows users to Interact with the model by providing Prompts and observing the generated output in real-time. This hands-on experience highlights the effectiveness and versatility of AI applications on Snapdragon devices.

The stable diffusion model not only produces high-quality outputs but also demonstrates the portability and ease of deployment offered by the Qualcomm AI stack. Developers can leverage the stack to build AI models that can be deployed effortlessly across different platforms, ensuring a consistent and reliable experience for end-users.

4. Future Investments in AI by Qualcomm

Qualcomm recognizes the potential of AI and the significance of continuous innovation in this field. The company has made substantial investments in AI research and development, aiming to drive advancements in AI technology and deliver cutting-edge solutions to developers and consumers alike.

Qualcomm envisions a future where AI is seamlessly integrated into various aspects of our lives. From productivity apps to automotive solutions, AI has the potential to revolutionize the way we work, live, and interact with technology. As the demand for AI continues to grow, Qualcomm remains committed to providing developers with the tools and resources they need to create innovative AI applications.

One of the key areas of focus for Qualcomm is the expansion of AI capabilities across different markets and industries. From productivity and entertainment to industrial applications and IoT, Qualcomm aims to enable developers to leverage AI in a wide range of use cases. By providing developers with powerful and efficient AI solutions, Qualcomm is at the forefront of driving AI adoption across industries.

On the technological front, Qualcomm is continuously exploring new ways to enhance the performance and efficiency of AI models. As AI models become more complex and demanding, Qualcomm is investing in custom silicon and advanced architectures to optimize the execution of AI workloads. The company is constantly pushing the boundaries of what is possible with AI on the edge, ensuring that developers can take full advantage of the latest hardware advancements.

5. Call to Action for Developers

Developers play a crucial role in shaping the future of AI and driving its adoption in various industries. Qualcomm recognizes the importance of developer engagement and offers a range of resources and opportunities for developers to explore and utilize AI in their applications.

To get started with the Qualcomm AI stack, developers are encouraged to visit the Qualcomm booth, where they can learn more about the stack, see live demos, and interact with experts. The booth provides a hands-on experience that showcases the power and versatility of Qualcomm's AI solutions.

Additionally, developers can also attend various AI-related sessions and workshops to gain a deeper understanding of how to leverage the Qualcomm AI stack in their applications. These sessions cover topics such as on-device AI leadership, hybrid AI, and optimizing AI models for the Onyx runtime.

Furthermore, Qualcomm offers resources like the Qualcomm AI Studio, which provides tutorials, guides, and documentation to help developers get started with AI development. Developers can access the AI Studio and explore the available tools and libraries to accelerate their AI development Journey.

In summary, Qualcomm offers developers a comprehensive ecosystem of tools, resources, and support to facilitate AI development. By leveraging the Qualcomm AI stack and engaging with the developer community, developers can unlock the full potential of AI and create innovative applications that enhance user experiences across a wide range of industries.

6. The Ubiquity of AI Accelerators in Hardware

The adoption of AI accelerators in hardware is rapidly increasing, with Qualcomm leading the way in providing AI capabilities in their Snapdragon devices. AI accelerators have become essential components in modern hardware, offering significant advantages in terms of latency, security, privacy, and economics.

As AI accelerators become more prevalent, developers and users can expect a more seamless and efficient experience in running AI models. The integration of AI capabilities directly into hardware, such as the Snapdragon Neural Processing Unit (NPU), enables developers to leverage the power of dedicated AI hardware to accelerate their applications.

The ubiquity of AI accelerators in hardware is a testament to the growing importance of AI in our everyday lives. From smartphones to laptops and IoT devices, AI is becoming an integral part of the user experience. By offloading AI workloads to dedicated hardware, developers can ensure optimal performance and energy efficiency while delivering innovative AI features to end-users.

Qualcomm's commitment to advancing AI capabilities in their hardware ensures that developers can rely on their Snapdragon devices to deliver high-performance AI solutions. As the demand for AI accelerators continues to grow, developers can expect a future where AI is seamlessly integrated into all aspects of our digital lives.

7. Backward Compatibility and Support for Legacy Silicon

To ensure compatibility across different generations of hardware, Qualcomm emphasizes backward compatibility and support for legacy silicon. As new products and chipsets are introduced, Qualcomm strives to maintain stability in their APIs and minimize disruptions for developers.

The Qualcomm AI stack is built with backward compatibility in mind, allowing developers to leverage their existing investments and ensure their AI applications run smoothly on both new and old hardware. Qualcomm's commitment to providing regular updates to their stack ensures that developers can Continue to innovate and optimize their applications without worrying about compatibility issues.

By supporting legacy silicon, Qualcomm enables developers to reach a larger audience and ensure their AI applications can be run on a wide range of devices. This compatibility helps to future-proof applications, allowing them to take advantage of advancements in silicon and reap the benefits of improved performance and efficiency.

Qualcomm also actively works with developers and partners to Gather feedback and address any compatibility challenges that may arise. This collaborative approach ensures that developers have a smooth transition when adopting new hardware or APIs, making it easier for them to bring their AI applications to market.

8. Q&A Session

Q: Will the Windows Snappy SDK be improved to include the tools so Linux is no longer required? For example, model conversion, quantization, etc.

A: Yes, Qualcomm plans to enhance the Windows Snappy SDK to provide native tooling for Windows, eliminating the dependency on Linux. This includes tools for model conversion, quantization, and other necessary tasks. These improvements will make it easier for developers to work with the Snappy SDK on Windows and streamline the development process.

Q: What's being done to have a native experience with Onyx runtime and move away from using DLC?

A: Qualcomm is actively working on integrating Onyx runtime more seamlessly with the Qualcomm AI stack. By leveraging Onyx runtime and reducing dependence on DLC (Downloadable Content), developers can have a more native and streamlined experience with the stack. This includes providing upgraded features and optimizing the integration of Onyx runtime with Qualcomm's AI Engine Direct based EP.

Q: What techniques are being used to move towards 10 billion parameters on the edge? Is 8-bit quantization the only technique, or are other techniques required?

A: Moving towards 10 billion parameters on the edge involves addressing multiple challenges, including model size and memory management. While 8-bit quantization is an important technique for reducing model size, other techniques are necessary to preserve accuracy and optimize performance. These techniques may include architecture design, custom silicon, efficient memory management, and more. Qualcomm is continuously researching and developing new approaches to enable running larger and more complex AI models on edge devices.

Q: When will the ubiquity of AI accelerators in hardware matter to most users? Will it be limited to certain tiers of PCs?

A: The ubiquity of AI accelerators in hardware already matters to users who are using Qualcomm Snapdragon devices or other platforms with dedicated AI accelerators. These users can enjoy the benefits of accelerated AI performance, enhanced features, and improved power efficiency. As AI accelerators become more widespread, their impact will extend to a wider range of devices and users. While higher-tier PCs and devices are currently leading the adoption of AI accelerators, it is expected that AI accelerators will become more prevalent in mainstream devices, providing a more consistent and enhanced AI experience for the majority of users.

9. Conclusion

In conclusion, Qualcomm is playing a pivotal role in enabling developers to leverage AI capabilities on the edge through their collaboration with Microsoft and the development of their Qualcomm AI stack. By bringing powerful AI solutions to Windows on Snapdragon devices and supporting the Onyx runtime, Qualcomm is empowering developers to create innovative AI applications that can run seamlessly across various platforms. With a focus on backward compatibility, support for legacy silicon, and continuous investments in AI, Qualcomm is paving the way for the future of AI on the edge. As AI accelerators become increasingly prevalent in hardware, developers can expect to unlock the full potential of AI and deliver exceptional user experiences across a wide range of industries and devices.

Highlights