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Machine learning can run directly on embedded devices, from capable Linux computers to small microcontrollers. The right deployment depends on the device’s memory, compute and power budget, its sensor inputs, and whether a suitable runtime supports the model’s operations. For sensor-equipped, battery-powered microcontrollers, this constrained approach is often called TinyML.
What is embedded machine learning?
Embedded machine learning (embedded ML) means deploying a model on a device that senses or acts on the physical world, or close to that device. Instead of continuously sending raw sensor data to a remote service, the device can process data locally and produce a result such as a classification, an anomaly alert, or a forecast.
TinyML describes the especially constrained end of embedded ML. The tinyML Foundation frames it as on-device sensor-data analytics at extremely low power—typically in the milliwatt range and below—often for always-on, battery-operated devices. That is a useful description, not a strict boundary for every embedded ML project. A Raspberry Pi running Linux can be an embedded ML device without being a tiny microcontroller project.
How do I run machine learning on a microcontroller?
A microcontroller workflow involves more than choosing a small model. You need representative sensor data, a model that fits the device, operations supported by the chosen runtime, and validation on the actual hardware. Google’s LiteRT for Microcontrollers overview describes a C++ library for running models on constrained devices.
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- Define the inference task. Decide what result is useful: for example, classify a sound, detect an unusual vibration, or forecast a sensor value.
- Collect representative sensor data. Sample from the actual sensor under the conditions the device will encounter. Account for signal processing and feature extraction, as well as changes in orientation, noise, operating state, or environment that matter to the task.
- Train and check a compact model. Model accuracy is only one requirement. Confirm that the model fits the target and uses operations supported by the target runtime.
- Convert and package the model. Google’s microcontroller workflow converts the model and stores it as a C byte array in read-only program memory.
- Integrate and run inference. Use the C++ library to execute inference, then connect the result to the device’s application logic—for example, logging a classification or triggering an alert.
- Validate on the target device. Check memory use and latency, and test the complete application with the real sensor stream. Measure power against the project’s budget; performance on a different processor or with a different model is not a reliable substitute.
This end-to-end check matters because the sensor, preprocessing, model, runtime, and application all affect whether a result is useful. The cited documentation does not establish a universal latency, memory, or power result for every combination.
What hardware do I need for an embedded ML project?
Start with the task and sensor, then compare candidate devices against the resources and integration the application requires. A board named by a runtime’s documentation is a possible starting point, not proof that it includes every sensor an example needs or that it is the best fit for your project.
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- Memory and storage: Check available RAM for inference and working data, as well as program memory for firmware and the model.
- Compute and power: Determine whether the processor can meet the application’s latency and duty-cycle needs within its power budget.
- Sensors and peripherals: Verify the required sensor interfaces, peripheral support, and sampling behavior for the data your model expects.
- Runtime compatibility: Confirm that the model’s operations and data types are supported and that suitable optimized kernels exist for the processor.
- Development effort: Account for the toolchain, language and API, low-level integration, and the work required to manage memory.
- Connectivity and data handling: Decide whether the device must keep working offline and whether the design should keep sensor data local.
Google’s LiteRT for Microcontrollers documentation lists boards including Arduino Nano 33 BLE Sense, SparkFun Edge, STM32F746 Discovery Kit, Adafruit EdgeBadge, Adafruit Circuit Playground Bluefruit, and Espressif ESP32-DevKitC. Consult its current support information before selecting a board, since support and availability can change. Google’s examples include a microphone-based “micro speech” model for recognizing “yes” and “no,” and person detection using camera data; these examples do not mean that every listed board has a microphone or camera built in.
How small does a model need to be for a microcontroller?
There is no single model-size threshold for all microcontrollers. The model must fit the target’s available program memory, and inference also needs working memory and compute. Runtime and application code take resources too, so a model that fits in isolation may still leave too little room for the complete firmware or its other tasks.
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Google says the core LiteRT for Microcontrollers runtime fits in 16 KB on an Arm Cortex-M3 and can run many basic models. That is the runtime footprint stated by Google—not the complete firmware or application size, and not a guarantee that a particular model will fit or run acceptably. Check the target’s memory limits, supported operations, and the footprint of the integrated application.
Which software should you use?
LiteRT for Microcontrollers
Google describes LiteRT for Microcontrollers as a C++ 17 library for 32-bit platforms. It is extensively tested on Arm Cortex-M processors and has been ported to other architectures, including ESP32. Its constrained-device workflow requires a model that fits and uses supported operations; expect low-level C++ integration and manual memory management.
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Standard LiteRT on embedded Linux
For a more capable embedded Linux device, Google says standard LiteRT may be easier to integrate. A Raspberry Pi is one example. More available resources and a Linux environment can make this a more practical route than a microcontroller runtime when the project’s model and integration needs exceed a small device’s constraints.
Arm CMSIS-NN optimized kernels
Arm CMSIS-NN provides optimized neural-network kernels for Cortex-M processor groups. It has separate implementations for processors without SIMD capability, with DSP extensions, and with Arm M-Profile Vector Extension instructions, and follows the cited int8 and int16 quantization specifications. It is a target-specific optimization library, not a complete modeling or deployment platform; processor fit and operation support still matter.
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Should you use a microcontroller or Raspberry Pi for edge AI?
Neither target is universally better. A microcontroller is a natural candidate when the task is small enough for its resources and low-power, sensor-oriented operation matters. An embedded Linux device such as a Raspberry Pi may be easier to integrate with standard LiteRT when the project benefits from a more capable platform. Compare the actual model, sensor, and application rather than choosing by category alone.
| Decision factor | Microcontroller | Embedded Linux device |
|---|---|---|
| Typical fit | Constrained sensor inference; LiteRT for Microcontrollers targets devices with only a few kilobytes of memory. | More capable embedded devices; Google gives Raspberry Pi as an example where standard LiteRT may be easier to integrate. |
| Runtime and integration | LiteRT for Microcontrollers uses C++ 17 and requires low-level integration and manual memory management. | Standard LiteRT may offer an easier integration path, depending on the device and project. |
| Model fit | Model must fit the target and use supported operations. | Check the platform’s resources and runtime compatibility for the actual model. |
| Best comparison to make | Available RAM and program memory, compute, power, sensor interfaces, supported operations, latency, and toolchain effort. | Model and application requirements, sensor integration, runtime fit, latency, power, connectivity, and data handling. |
The table describes platform trade-offs, not a performance ranking. Actual latency, memory use, and energy consumption depend on the selected device, model, runtime, and application.
Can machine learning run without an internet connection?
Yes. A model deployed locally can perform inference without depending on a reliable internet connection, provided the device has the data and resources it needs. Local inference can also keep sensor data on the device, which may help preserve privacy because the data need not leave it. That architecture is not, by itself, a guarantee of privacy or security; data handling elsewhere in the application still matters.
Quick Recap
What are the main limitations?
- Model and operation constraints: A model must fit the target, and the runtime must support its operations. Conversion alone does not make every model usable on every device.
- Device and software constraints: Microcontroller deployments have limited device and operation support. They can require low-level C++ work and manual memory management.
- No on-device training in the documented microcontroller path: LiteRT for Microcontrollers is for inference, not training a model on the device.
- Project-specific power and latency: Local inference does not automatically save energy or meet timing needs. Test the integrated application on its intended hardware and sensor stream.
- Privacy is not automatic: Keeping data local can reduce data transfer, but the overall design determines how data is stored, exposed, or transmitted.
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