Whether you're using a Mac or a PC, it's important to maximize your deep learning performance. I'm David Shaw, and in this episode of AI News we'll look at Apple machine learning frameworks or Core ML, and metal performance shaders, or MPS, on Intel processor graphics.
Core ML, available on Apple devices, is the main framework for accelerating domain-specific ML inference capabilities such as image analysis, object detection, and natural language processing. With Core ML, it takes advantage of Intel processors and Intel processor graphics to build and run machine learning workloads right on your device. This removes the dependency on network connectivity, security, and even privacy concerns.
Core ML is built on top of low-level frameworks such as MPS, the Intel processor graphics, and basic neural network subroutines Intel processors. They're highly tuned and optimized for Intel hardware. MPS is the main building block for Core ML to run machine learning workloads on GPUs.
As an application developer, you can write your own application to use the MPS API directly to target underlying GPU devices. Check out this article to see the benefits of using Core ML and MPS API on Mac OS platforms. You'll learn how to take full advantage of the underlying Intel processor graphics architecture.
In addition, read the performance case study. It shows the techniques used in achieving high hardware efficiency, all using the highly optimized MPS primitives for Intel processor graphics on Apple Mac OS platforms. See the links provided, and I'll see you next week.