Overview

We support any Edge AI Hardware that can run C++, and more!

You will find on this page a list of edge AI hardware targets that are either maintained by Edge Impulse or by our partners. During the integration and when possible, we leverage and integrate the hardware capabilities (optimized floating point units (FPU), DSP and Neural Network accelerations, GPU or other AI accelerators).

MCU-based hardware

For the MCU-based hardware, depending on the integration we provide several or all of the following options:

• A default Edge Impulse firmware, ready to be flashed on the hardware. The firmware capabilities depends on the integration (see also Edge Impulse firmwares).:

  • Data collection: Enables to connect the hardware to Edge Impulse Studio to simplify your getting started journey and ease the data collection from some or all the sensors available.

  • Inferencing example: This includes the data sampling, extracting features using the signal processing blocks and run the inference using learning blocks.

  • Serial protocol and/or remote management protocole.

  • The open-source code for the firmware, which comes with documentation on how to build and compile the Edge Impulse firmware.

• Examples on how to integrate your Impulse with your custom firmware, either using the C++ inferencing SDK or using libraries or components tailored for your hardware development environments. In our Github repository, search for the example-standalone-inferencing-%target%

• Integrated deployment options to directly export a ready-to-flash Edge Impulse firmware packaged with your Impulse (including both the signal processing and the machine learning model).

• Profiling (estimation of memory, flash and latency) available in Edge Impulse Studio and in the Edge Impulse Python SDK.

• Extensive hardware testing, to make sure any improvements and changes in Edge Impulse will not break the current integration.

Not on the list?

If you are using a different hardware target or custom PCB? No problem!

You can upload data to Edge Impulse in a variety of ways, such as using the Data forwarder, the Edge Impulse for Linux SDK, or by uploading files directly (e.g. CSV, JPG, WAV).

From there, your trained model can be deployed as a C++ library. It can require some effort, but most build systems (for computers, smartphones, and microcontrollers) will work with our C++ library. This, of course, requires that your build system has a C++ compiler and that there is enough flash/RAM on your device to run the library/model. And although we leverage hardware acceleration when possible on the hardware listed in this section, keep in mind that our EON Compiler will optimize your preprocessing and your ai models for any targets compared to traditional compiler options.

Also, if you feel like porting the official Edge Impulse firmware to your own board, use this porting guide.

Linux-based hardware

For the Linux-based hardware, depending on the integration we provide several or all of the following options:

• The Edge Impulse Linux CLI: It contains tools that let you collect data from any microphone or camera, download the .eim (Edge Impulse Models) or run a test application to classify your data, available on your terminal or through a web interface.

• Deployment options:

  • Linux .eim, Edge Impulse for Linux models are delivered in .eim format. This is an executable that contains your signal processing and ML code, compiled with optimizations for your processor, GPU or other AI accelerators.

  • Docker container, for environments supporting containerized workloads, facilitating deployment on gateways or in the cloud with full hardware acceleration for most Linux targets.

• Linux Inferencing SDKs: To build your own applications, or collect data from new sensors, you can use the high-level language SDKs. These use full hardware acceleration, and let you integrate your Edge Impulse models in a few lines of code

• Profiling (estimation of memory, flash and latency), available in Edge Impulse Studio and in the Edge Impulse Python SDK.

Not on the list?

Different development board? Probably no problem! You can use the Linux x86_64 getting started guide to set up the Edge Impulse for Linux CLI tool or use the Docker, and you can run your impulse on any x86_64, ARMv7 or AARCH64 Linux target. And although we leverage hardware acceleration when possible on the hardware listed in this section, keep in mind that our EON Compiler will optimize your preprocessing and your ai models for any targets compared to traditional compiler options.

You can upload data to Edge Impulse in a variety of ways, such as using the Data forwarder, the Edge Impulse for Linux SDK, or by uploading files directly (e.g. CSV, JPG, WAV).

The hardware targets listed in this section are the perfect way to start building machine learning solutions on real embedded hardware. Edge Impulse's Solution Engineers and Embedded Engineers have a strong expertise with these hardware targets and can help on your integration. Feel free to contact us.

If you just want to experience Edge Impulse? You can also use your Mobile phone!

Edge AI Hardware

MCU

• Nordic Semi nRF52840 DK (nRF52840 | Cortex-M4F 64MHz)
• Nordic Semi nRF5340 DK (nRF5340 | Cortex-M33 128MHz)
• Nordic Semi nRF9160 DK (nRF9160 | Cortex-M33 64MHz)
• Nordic Semi nRF9161 DK (nRF9160 | Cortex-M33 64MHz)
• Nordic Semi nRF9151 DK (nRF9160 | Cortex-M33 64MHz)
• Nordic Semi nRF7002 DK (nRF7002 | Cortex-M33 128MHz)
• Nordic Semi Thingy:53 (nRF5340 | Cortex-M33 128MHz)
• Nordic Semi Thingy:91 (nRF9160 | Cortex-M33 64MHz)

CPU

• macOS (x86, M1, M2)
• Linux x86_64 (x86_64)