Inside the M4 Apple Neural Engine, Part 1: Reverse Engineering (maderix.substack.com)

Article summary

The article describes the reverse engineering of the M4 Apple Neural Engine, a graph execution engine that executes neural network graphs as one atomic operation. The authors bypassed CoreML and talked directly to the hardware, discovering how to compile and execute programs on the ANE without CoreML. They also measured the true peak performance of the ANE, finding it to be different from Apple's claimed 38 TOPS. The authors used a combination of techniques, including class discovery, method swizzling, and binary analysis, to understand the ANE's software stack and hardware capabilities.

Main themes

• Apple Neural Engine
• Reverse Engineering
• Machine Learning
• Hardware Acceleration
• CoreML
• AI Writing Style

What commenters say

• The Apple Neural Engine is useful for various tasks beyond Apple's branded intelligence features, such as object recognition and image manipulation.
• The use of AI-generated writing is becoming more prevalent and may start to influence human writing styles, potentially leading to a loss of unique human voice.
• The article's writing style, which includes phrases like 'The key insight' and 'And one delightful discovery', is characteristic of AI-generated content and may be seen as annoying or hubristic.
• The claimed performance of the Apple Neural Engine is disputed, with some arguing that Apple's marketing claims are misleading and not reflective of the hardware's actual capabilities.
• The ability to use the Apple Neural Engine for tasks beyond Apple's official use cases is limited by the lack of transparency and control over the hardware.
• The future of software engineering will be shaped by the use of AI acceleration, which will enable developers to achieve more with their skills.
• The article's findings on the Apple Neural Engine's performance and capabilities are interesting, but the writing style and potential AI influence are distracting and take away from the author's voice.
• The use of AI-generated content is not necessarily a bad thing, as it can help technical people communicate complex ideas more effectively, but it may also lead to a homogenization of writing styles.