What new instructions are in the M4 chip?
Generated on 7/31/2024
1 search
The new instructions in the M4 chip were not explicitly detailed in the provided context. However, there are several enhancements and optimizations mentioned for Apple silicon, particularly in the context of machine learning and AI models. These include:
- Stateful KV Cache: This improves the efficiency of key and value preparation for attention mechanisms in transformer models.
- Fused Representation for Scaled Dot Product Attention (SDPA): This optimization allows for more efficient computation by taking inputs all at once, rather than breaking them into smaller operations.
- Model Compression Techniques: New techniques have been introduced to optimize models for Apple hardware, balancing storage size, latency, and accuracy.
For more detailed information on these optimizations, you can refer to the following sessions:
- Bring your machine learning and AI models to Apple silicon (17:08)
- Train your machine learning and AI models on Apple GPUs (01:36)
If you need more specific details about the M4 chip instructions, you might want to check the full sessions or additional resources provided by Apple.
Platforms State of the Union 5-Minute Recap
Watch a quick recap of the newest advancements on Apple platforms.
Bring your machine learning and AI models to Apple silicon
Learn how to optimize your machine learning and AI models to leverage the power of Apple silicon. Review model conversion workflows to prepare your models for on-device deployment. Understand model compression techniques that are compatible with Apple silicon, and at what stages in your model deployment workflow you can apply them. We’ll also explore the tradeoffs between storage size, latency, power usage and accuracy.
Platforms State of the Union
Discover the newest advancements on Apple platforms.
Explore machine learning on Apple platforms
Get started with an overview of machine learning frameworks on Apple platforms. Whether you’re implementing your first ML model, or an ML expert, we’ll offer guidance to help you select the right framework for your app’s needs.
Port advanced games to Apple platforms
Discover how simple it can be to reach players on Apple platforms worldwide. We’ll show you how to evaluate your Windows executable on Apple silicon, start your game port with code samples, convert your shader code to Metal, and bring your game to Mac, iPhone, and iPad. Explore enhanced Metal tools that understand HLSL shaders to validate, debug, and profile your ported shaders on Metal.
Train your machine learning and AI models on Apple GPUs
Learn how to train your models on Apple Silicon with Metal for PyTorch, JAX and TensorFlow. Take advantage of new attention operations and quantization support for improved transformer model performance on your devices.