ane
An experiment that trains neural networks directly on the Apple Neural Engine using reverse-engineered private APIs. It explores training on hardware normally used only for inference.
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Share on XLicense: MIT
Overview
This project is a research experiment that trains neural networks directly on Apple's Neural Engine, which Apple limits to inference through CoreML. It reaches the hardware through reverse-engineered private APIs, _ANEClient and _ANECompiler, without CoreML training, Metal or the GPU. It also documents measured ANE throughput, power and SRAM behavior.
Key features
- Backpropagation on the Apple Neural Engine
- Uses reverse-engineered private APIs, no CoreML training or GPU
- Benchmarks of ANE throughput, power and SRAM behavior
- Reference for direct ANE access outside CoreML
Best for
Researchers and tinkerers exploring what the Neural Engine can do. The author states it is a research project, not a production framework.
- Upstream
- maderix/ANE
- Fork on GitHub
- Guo-astro/ane
- Upstream stars
- 7.3k
- Category
- Hardware, OS and embedded
- Language
- Objective-C
- License
- MIT
- Forked
- 2026-03-02
- Sync status
- In syncLast synced 2026-10-20
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