# heliaRT

Keep your LiteRT surface. Swap the backend underneath.
Your model and application stay at the center. heliaRT manages graph execution, while the selected backend supplies the operator implementations.

Start with the tools you use.
CMake, Zephyr, neuralSPOT-X source builds and CMSIS-Pack share one source manifest and backend-selection rules. Choose the integration that fits your application.

- [Zephyr](https://ambiqai.github.io/helia-rt/getting-started/choose-your-path/#zephyr): Add the module to your west workspace and configure the backend through Kconfig.
- [neuralSPOT-X](https://ambiqai.github.io/helia-rt/getting-started/choose-your-path/#neuralspot-x): Integrate the runtime with your neuralSPOT-X application’s build.
- [CMSIS-Pack](https://ambiqai.github.io/helia-rt/getting-started/choose-your-path/#cmsis-pack): Package runtime sources for a CMSIS-based project.
- [Source and CMake](https://ambiqai.github.io/helia-rt/getting-started/choose-your-path/#source-and-cmake): Build the runtime or link a compatible release archive into your own application.

Build choices
Choose what your firmware needs.
Use a matching release archive for a fixed configuration, or build from source to select the backend, floating-point features and kernel profiles.
Configure your build →

Source or prebuiltOwn the build configuration, or link an archive whose settings match your application.
SPEED or SIZESelect the HELIA kernel profile in source builds, with per-family overrides where available.
GCC · Arm Compiler 6 · ATfEMatch the library and application toolchain. Compiler choice and kernel profile are separate decisions.

Choose the inference path that fits.
For supported models, heliaAOT is the recommended option when targeting latency, power and memory efficiency. Use heliaRT when your application calls for the LiteRT interpreter and its runtime integration.

- [heliaAOT](https://ambiqai.github.io/helia-aot/): Compile a supported model into standalone inference code. Check model compatibility in the heliaAOT documentation.
- [heliaRT](https://ambiqai.github.io/helia-rt/guide/runtime/): Load a model, register its operators and manage the tensor arena through the familiar MicroInterpreter API.

Take the next step.
Bring an existing application across, run a checked first inference, or understand a model's requirements before integrating it.

- [First inference](https://ambiqai.github.io/helia-rt/getting-started/first-inference/): Follow model validation, operator registration, tensor allocation and invocation in order.
- [Migrate from LiteRT](https://ambiqai.github.io/helia-rt/getting-started/migrate-from-litert/): Keep a known model baseline and verify the runtime, build and output contracts together.
- [Check compatibility](https://ambiqai.github.io/helia-rt/guide/model-compatibility/): Understand tensor types, floating-point features and allocation versus invocation failures.