helia-aot 0.16.0

An ahead-of-time nn compiler for Ambiq SoCs.

heliaAOT

Bring blazing-fast, ultra-compact neural inference to Ambiq’s ultra-low-power SoCs—without a runtime.

heliaAOT is an ahead-of-time (AOT) compiler that converts front-end models (e.g., TFLite/ONNX) into stand-alone C inference modules optimized for Ambiq microcontrollers. By shifting codegen to build-time and eliminating the runtime interpreter, heliaAOT delivers compact, readable C with zero arena-guesswork and no dead code.

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✨ Why heliaAOT?

• Smaller binaries – Generate only the code you need. No interpreter, no dead kernels.
• Deterministic memory – Context-level memory planning; stop guessing arena sizes.
• Readable output – Layer-mirrored C files for fast bring-up and debugging.
• Ambiq-tuned – Optimized for Cortex-M (M4/M55/Helium) on Ambiq SoCs.
• Production-ready – Drop into Zephyr/neuralSPOT or your existing build system.

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🚀 Quick Start

Option A: Download a standalone CLI

Prebuilt binaries (no Python required):

• macOS: helia-aot
• Linux: helia-aot
• Windows: helia-aot.exe

See the Releases page for downloads and checksums.

Run:

./helia-aot --help

Option B: Install via pipx

pipx install --python python3.12 "git+https://github.com/AmbiqAI/helia-aot.git@main"
helia-aot --help

We recommend pipx because it installs Python apps in isolated, self-contained environments and puts the CLI on your PATH.

Option C: Install from source

git clone https://github.com/AmbiqAI/helia-aot.git
cd helia-aot
uv sync
uv run helia-aot --help

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🧭 First Conversion

helia-aot convert \
  --model.path ./models/ad01_int8.tflite \
  --module.path ./out \
  --module.name ad01_aot

What you get:

out/ad01_aot/
├── LICENSE               # License for the generated module
├── README.md             # Module usage notes
├── includes-api/         # Public C headers
├── src/                  # Operator implementations
└── module.mk

Drop the folder into your firmware tree and build like any other C module.

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🛠️ Current CLI

heliaAOT currently exposes a single primary command:

• convert – Convert a TFLite/ONNX model into a C inference module.

  helia-aot convert --help
  

  We follow “progressive disclosure of complexity.” The CLI starts simple; advanced flags and expert workflows are documented in the site’s How-to and Reference sections.

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📚 Documentation

• Getting Started: the fastest way from model → C module
• How-To: step-by-step recipes for common tasks
• Reference: full list of commands/flags
• Guides: end-to-end projects and best practices
• Benchmarks: performance and size comparisons
• API: for power users who integrate deeper

The docs are written for CLI users first; you don’t need to know (or care) that the tool is implemented in Python.

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🔌 Supported Inputs & Targets

Inputs

• LiteRT/TFLite (int8/int16)

Targets

• Ambiq SoCs (Cortex-M4, M55/Helium)
• Integration paths: Zephyr module, neuralSPOT, or make/CMake projects

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🧩 What the Generated C Looks Like

• A compact operator library: only kernels required by your model.
• A structured init() / run() style API with clear I/O.
• Compile-time tensor shapes and a fixed memory plan.
• Optional operator callbacks for instrumentation/telemetry.

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🧪 Testing & Quality

• Continuous Integration runs linting and unit tests
• Deterministic builds and reproducible outputs where possible
• Example apps and benchmarks in the docs

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⚙️ Development (optional)

If you do want to contribute or run from source:

# Using uv (fast Python package manager)
uv sync
uv run helia-aot --help

# Or with pip + virtualenv
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
helia-aot --help

See CONTRIBUTING.md for coding standards, testing, and PR guidelines.

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🗺️ Roadmap

The project roadmap and features can be found on the GitHub Projects page.

We prioritize features that keep binaries tiny, predictable, and fast.

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💬 Support & Feedback

• Issues / bugs: Create an issue on GitHub
• Feature requests and discussions: Join the conversation

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📄 License

heliaAOT generates C modules that include a license header restricting use to Ambiq hardware. See the generated module’s LICENSE.txt for terms and the repository’s LICENSE for tooling.

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Ready to dive in? Head over to the Getting Started guide and generate your first module in minutes.