Apple Neural Engine: Architecture, Programming, and Performance
This reverse-engineered account of the Apple Neural Engine provides unprecedented technical details that could inform hardware design, AI performance optimization, and security research.
- The ANE is a fixed-function matrix accelerator in Apple's A11-class iPhone/iPad chips and M1-class Mac chips since their release
- The guide documents the engine’s datapath, roofline performance bounds, dispatch route below Core ML framework, compiler, on-disk program format, weight-compression scheme, kernel driver, firmware, and command protocol
- Covers A11 through A18 and M1 through M5 families with per-chip target tables and operation-by-device matrix
Full summary
The article presents a reverse-engineered account of the Apple Neural Engine (ANE), detailing its architecture, programming interfaces, and performance characteristics. It covers the ANE's presence in various Apple silicon families from A11 to M5, including direct measurements on M1 and M5 chips. The guide documents the engine’s datapath, roofline performance bounds, dispatch route below Core ML framework, compiler, on-disk program format, weight-compression scheme, kernel driver, firmware, and command protocol. Claims are categorized as measured, decompiled-derived, or predicted to ensure transparency.