NWNetwork
Generated on 8/12/2024
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It seems like you're asking about "NWNetwork," but the context provided doesn't mention anything related to NWNetwork. However, I can provide information on the sessions mentioned in the context, which focus on machine learning and ad attribution technologies presented at WWDC.
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Accelerate machine learning with Metal: This session introduces the NP's graph viewer, a new tool in Xcode 16 for visualizing machine learning models on Apple silicon. It covers how to create and optimize NP's graph packages and demonstrates the use of adapters to compensate for quantization errors.
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Meet AdAttributionKit: This session discusses the new AdAttributionKit, which is built on SkadNetwork fundamentals. It covers topics such as fetching ads, displaying ads, postbacks, and re-engagement, providing a comprehensive overview of ad attribution processes.
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Support real-time ML inference on the CPU: This session introduces the BNNS Graph API, which optimizes machine learning models for real-time use cases on the CPU. It highlights features like layer fusion and memory optimization to enhance performance.
If you have specific questions about these sessions or need timestamps for particular topics, feel free to ask!
Meet AdAttributionKit
Introducing AdAttributionKit, a new iOS framework for privacy-preserving ad attribution. Learn how AdAttributionKit supports re-engagement, click-through attribution (including support for custom creative), JWS formatted impressions and postbacks, and more. We’ll provide insight into testing your AdAttributionKit implementation and deep-dive into code snippets and best practices for adopting AdAttributionKit.
Accelerate machine learning with Metal
Learn how to accelerate your machine learning transformer models with new features in Metal Performance Shaders Graph. We’ll also cover how to improve your model’s compute bandwidth and quality, and visualize it in the all new MPSGraph viewer.
Support real-time ML inference on the CPU
Discover how you can use BNNSGraph to accelerate the execution of your machine learning model on the CPU. We will show you how to use BNNSGraph to compile and execute a machine learning model on the CPU and share how it provides real-time guarantees such as no runtime memory allocation and single-threaded running for audio or signal processing models.