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.
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 2026-09-29
More in Hardware, OS and embedded
The original Apollo 11 Guidance Computer source code for the command and lunar modules, published for reading and historical study.
Forked 2026-08-31Last synced 2026-09-29Custom license (see repository)Hardware, OS and embeddedGitHub
A teaching operating system written in exactly 2000 lines of code so students can read all of it. It runs on QEMU and RISC-V boards and comes with course projects.
Forked 2026-08-20Last synced 2026-09-29Custom license (see repository)Hardware, OS and embeddedGitHub
A 100-day series of IoT and embedded projects using ESP32, ESP8266 and Raspberry Pi Pico with MicroPython. Each day covers one sensor or module with code, circuit diagram and explanation.
Forked 2026-08-17Last synced 2026-09-29No license declaredHardware, OS and embeddedGitHub
An open-source, low-cost 10.5 GHz phased array radar project with hardware designs, firmware and software. It targets researchers, drone developers and SDR hobbyists who want to experiment with radar.
Forked 2026-08-17Last synced 2026-09-29Custom license (see repository)Hardware, OS and embeddedGitHub