The Ensemble series of fusion processors from Alif Semiconductor utilize ARM's low power Cortex-M55 CPUs with dedicated Ethos-U55 microNPUs to run embedded ML workloads quickly and efficiently. The devices feature both 'High Power' cores designed for large model architectures, as well as 'High Efficiency' cores designed for low power continuous monitoring. The E7 Development kit and E7 AppKit are both fully supported by Edge Impulse. The Ensemble E7 kits feature multiple core types, dual MEMS microphones, accelerometers, and a MIPI camera interface.

To get started with the Alif Ensemble E7 and Edge Impulse you'll need the following hardware:

• Alif Ensemble E7 AppKit OR Alif Ensemble E7 Development Kit

Installing dependencies

To set this device up in Edge Impulse, you will need to install the following software:

1. Edge Impulse CLI.

2. The latest Alif Security Toolkit:

• Navigate to the Alif Semiconductor Kit documentation page (you will need to register to create an account with Alif, or log in to your existing Alif account). and download the latest App Security Toolkit (tested with version 0.56.0) for windows or linux.

• Extract the archive, and read through the included Security Toolkit Quick Start Guide to finalize the installation

1. (Optional) Docker Desktop:

• If you are using MacOS, we recommended installing Docker Desktop in order to use the Alif Security Toolkit for programming.

Connecting to Edge Impulse

Once you have installed it's time to connect the development board to Edge Impulse.

1. Configure your hardware

To interface the Alif Ensemble E7 AppKit or Development Kit, you'll need to make sure your hardware is properly configured and connected to your computer. Follow the steps below to prepare your specific kit for connection to Edge Impulse

2. Flash the default firmware to the device

After configuring the hardware, the next step is to flash the default Edge Impulse Firmware. This will allow us to collect data directly from your Ensemble device. To update the firmware:

1. Download the latest Edge Impulse firmware binary and unzip the file.

2. Navigate to the directory where you installed the Alif Security Toolkit

3. Copy the .bin files from the Edge Impulse firmware directory into the build/images directory of the Alif Security Toolkit

4. Copy all .json files from the Edge Impulse firmware directory into the build/config directory of the Alif Security Toolkit

5. From a command prompt or terminal, run the following commands:

./app-gen-toc -f build/config/ei-appkit-gcc.json
./app-write-mram

3. Run the Edge Impulse CLI!

Now, the Ensemble device can connect to the Edge Impulse CLI installed earlier. To test the CLI for the first time, either:

Create a new project from the Edge Impulse project dashboard

OR

Clone an existing Edge Impulse public project, like this Face Detection Demo. Click the link and then press Clone at the top right of the public project.

Then, from a command prompt or terminal on your computer, run:

edge-impulse-daemon

This will start a wizard which will ask you to log in and choose an Edge Impulse project. You should see your new or cloned project listed on the command line. Use the arrow keys and hit Enter to select your project.

5. Verifying that the device is connected

That's all! Your device is now connected to Edge Impulse. To verify this, go to

Next steps: building a machine learning model

With everything set up you can now build your first machine learning model with these tutorials. This will walk you through the process of collecting data and training a new ML model:

• Adding sight to your sensors

• Object detection

• Counting objects using FOMO

Alternatively, you can test on-device inference with a demo model included in the base firmware binary. To do this, you may run the following command from your terminal:

edge-impulse-run-impulse --debug

Then, once you've tested out training and deployment with the Edge Impulse Firmware, learn how to integrate impulses with your own custom Ensemble based application:

• Running your impulse locally on the Alif Ensemble Series

The Nicla Sense ME is a tiny, low-power tool that sets a new standard for intelligent sensing solutions. With the simplicity of integration and scalability of the Arduino ecosystem, the board combines four state-of-the-art sensors from Bosch Sensortec:

• BHI260AP motion sensor system with integrated AI.

• BMM150 magnetometer.

• BMP390 pressure sensor.

• BME688 4-in-1 gas sensor with AI and integrated high-linearity, as well as high-accuracy pressure, humidity and temperature sensors.

Designed to easily analyze motion and the surrounding environment – hence the “M” and “E” in the name – it measures rotation, acceleration, pressure, humidity, temperature, air quality and CO2 levels by introducing completely new Bosch Sensortec sensors on the market.

Its tiny size and robust design make it suitable for projects that need to combine sensor fusion and AI capabilities on the edge, thanks to a strong computational power and low-consumption combination that can even lead to standalone applications when battery operated.

The Arduino Nicla Sense ME is available for around 55 USD from the .

Installing dependencies

To set this device up in Edge Impulse, you will need to install the following software:

3. On Linux:

   • GNU Screen: install for example via sudo apt install screen.

Connecting to Edge Impulse

With all the software in place it's time to connect the development board to Edge Impulse.

1. Connect the development board to your computer

Use a micro-USB cable to connect the development board to your computer.

2. Update the firmware

The development board does not come with the right firmware yet. To update the firmware:

2. Open the nicla_sense_ingestion.ino sketch in a text editor or the Arduino IDE.

3. For data ingestion into your Edge Impulse project, at the top of the file, select 1 or multiple sensors by un-commenting the defines and select a desired sample frequency (in Hz). For example, for the Environmental sensors:
4. Then, from your sketch's directory, run the Arduino CLI to compile:
5. Then flash to your Nicla Sense using the Arduino CLI:
6. Wait until flashing is complete, and press the RESET button once to launch the new firmware.

3. Data forwarder

From a command prompt or terminal, run:

This will start a wizard which will ask you to log in, and choose an Edge Impulse project. You will also name your sensor's axes (depending on which sensor you selected in your compiled nicla_sense_ingestion.ino sketch). If you want to switch projects/sensors run the command with --clean. Please refer to the table below for the names used for each axis corresponding to the type of sensor:

Note: These exact axis names are required to run the Edge Impulse Arduino library deployment example applications for the Nicla Sense without any changes.

Else, when deploying the model, you will see an error like the following:

If your axis names are different, when using the generated Arduino Library for the inference, you can modify the eiSensors nicla_sensors[] (near line 70) in the sketch example to add your custom names. e.g.:

4. Verifying that the device is connected

Next steps: building a machine learning model

Deploying back to device

With the impulse designed, trained and verified you can deploy this model back to your Arduino Nicla Sense ME. This makes the model run without an internet connection, minimizes latency, and runs with minimum power consumption. Edge Impulse can package the complete impulse - including the signal processing code, neural network weights, and classification code - up into a single library that you can run on your development board.

Overview

* Maybe not available on the board and require to attach additional sensors, see the dedicated page for more information.

** RAM used for the latency calculation, may differ from the datasheet.

*** ROM used for the latency calculation, may differ from the datasheet.

The Arduino Nicla Voice is a development board with a high-performance microphone and IMU, a Cortex-M4 Nordic nRF52832 MCU and the Syntiant NDP120 Neural Decision Processor™. The NDP120 supports multiple Neural Network architectures and is ideal for always-on low-power speech recognition applications. You'll be able to sample raw data, build models, and deploy trained embedded machine learning models directly from the Edge Impulse studio to create the next generation of low-power, high-performance audio interfaces.

The Edge Impulse firmware for this development board is open source and hosted on GitHub.

Installing dependencies

To set this device up in Edge Impulse, you will need to install the following software:

• Arduino CLI

• Edge Impulse CLI

Connecting to Edge Impulse

1. Download the firmware

Download the Nicla Voice firmware for audio or IMU below and connect the USB cable to your computer:

• Audio firmware

• IMU firmware

The archive contains different scripts to flash the firmware on your OS, ie for macOS:

• install_lib_mac.command: script will install the Arduino Core for the Nicla board and the pyserial package required to update the NDP120 chip. You only need to run this script once.

• flash_mac.command: to flash both the MCU and NDP120 chip. You should use this script on a brand new board

The additional scripts below can be used for specific actions:

• flash_mac_mcu.command: to flash only the Nordic MCU, ie if you recompiled the firmware and doesn't need to update the NDP120 model.

• flash_mac_model.command: to flash only the NDP120 model.

• format_mac_ext_flash.command: to format the external flash that contains the NDP120 model

2. Setup the Arduino Nicla Voice Board to collect data

After flashing the MCU and NDP chips, connect the Nicla Voice directly to your computer's USB port. Linux, Mac OS, and Windows platforms are supported. From a command prompt or terminal, run:

edge-impulse-daemon

This will start a wizard which will ask you to log in and choose an Edge Impulse project. If you want to switch projects run the command with --clean.

Alternatively, recent versions of Google Chrome and Microsoft Edge can collect data directly from your development board, without the need for the Edge Impulse CLI. See this blog post for more information.

That's all! Your device is now connected to Edge Impulse. To verify this, go to your Edge Impulse project, and click Devices. The device will be listed here.

Next steps: building a machine learning model

With everything set up you can now build your first machine learning model and evaluate it using the Arduino Nicla Voice Board with this tutorial:

• Responding to your voice - Syntiant (RC Commands)

• Motion Recognition - Syntiant

FAQ

• How to use Arduino-CLI with macOS M1 chip? You will need to install Rosetta2 to run the Arduino-CLI. See details on Apple website.

• How to label my classes? The NDP chip expects one and only negative class and it should be the last in the list. For instance, if your original dataset looks like: yes, no, unknown, noise and you only want to detect the keyword 'yes' and 'no', merge the 'unknown' and 'noise' labels in a single class such as z_openset (we prefix it with 'z' in order to get this class last in the list).

The Arduino Nano 33 BLE Sense is a tiny development board with a Cortex-M4 microcontroller, motion sensors, a microphone and BLE - and it's fully supported by Edge Impulse. You'll be able to sample raw data, build models, and deploy trained machine learning models directly from the studio. It's available for around 30 USD from Arduino and a wide range of distributors.

You can also use the Arduino Tiny Machine Learning Kit to run image classification models on the edge with the Arduino Nano and attached OV7675 camera module (or connect the hardware together via jumper wire and a breadboard if purchased separately).

The Edge Impulse firmware for this development board is open source and hosted on GitHub: edgeimpulse/firmware-arduino-nano-33-ble-sense.

Installing dependencies

To set this device up in Edge Impulse, you will need to install the following software:

1. Edge Impulse CLI.

2. Arduino CLI.

   • Here's an instruction video for Windows.

   • The Arduino website has instructions for macOS and Linux.

3. On Linux:

   • GNU Screen: install for example via sudo apt install screen.

Connecting to Edge Impulse

With all the software in place it's time to connect the development board to Edge Impulse.

1. Connect the development board to your computer

Use a micro-USB cable to connect the development board to your computer. Then press RESET twice to launch into the bootloader. The on-board LED should start pulsating to indicate this.

2. Update the firmware

The development board does not come with the right firmware yet. To update the firmware:

1. Download the latest Edge Impulse firmware, and unzip the file.

2. Open the flash script for your operating system (flash_windows.bat, flash_mac.command or flash_linux.sh) to flash the firmware.

3. Wait until flashing is complete, and press the RESET button once to launch the new firmware.

3. Setting keys

From a command prompt or terminal, run:

edge-impulse-daemon

This will start a wizard which will ask you to log in, and choose an Edge Impulse project. If you want to switch projects run the command with --clean.

Alternatively, recent versions of Google Chrome and Microsoft Edge can collect data directly from your development board, without the need for the Edge Impulse CLI. See this blog post for more information.

4. Verifying that the device is connected

That's all! Your device is now connected to Edge Impulse. To verify this, go to your Edge Impulse project, and click Devices. The device will be listed here.

Next steps: building a machine learning model

With everything set up you can now build your first machine learning model with these tutorials:

• Responding to your voice

• Recognize sounds from audio

• Adding sight to your sensors

• Object detection

• Counting objects using FOMO

Looking to connect different sensors? The Data forwarder lets you easily send data from any sensor into Edge Impulse.

Troubleshooting

Connecting an off-the-shelf OV7675 camera module

You will need the following hardware:

• Arduino Nano 33 BLE Sense board with headers.

• OV7675 camera module.

• Micro-USB cable.

• Solderless breadboard and female-to-male jumper wires.

First, slot the Arduino Nano 33 BLE Sense board into a solderless breadboard:

With female-to-male jumper wire, use the following wiring diagram, pinout diagrams, and connection table to link the OV7675 camera module to the microcontroller board via the solderless breadboard:

Download the full pinout diagram of the Arduino Nano 33 BLE Sense here.

Finally, use a micro-USB cable to connect the Arduino Nano 33 BLE Sense development board to your computer.

Now build & train your own image classification model and deploy to the Arduino Nano 33 BLE Sense with Edge Impulse!

The Nicla Vision is a ready-to-use, standalone camera for analyzing and processing images on the Edge. Thanks to its 2MP color camera, smart 6-axis motion sensor, integrated microphone, and distance sensor, it is suitable for asset tracking, object recognition, and predictive maintenance. Some of its key features include:

• Powerful microcontroller equipped with a 2MP color camera

• Tiny form factor of 22.86 x 22.86 mm

• Integrated microphone, distance sensor, and intelligent 6-axis motion sensor

• Onboard Wi-Fi and Bluetooth® Low Energy connectivity

• Standalone when battery-powered

• Expand existing projects with sensing capabilities

• Enable fast Machine Vision prototyping

• Compatible with Nicla, Portenta, and MKR products

Its exceptional capabilities are supported by a powerful STMicroelectronics STM32H747AII6 Dual ARM® Cortex® processor, combining an M7 core up to 480 Mhz and an M4 core up to 240 Mhz. Despite its industrial strength, it keeps energy consumption low for battery-powered standalone applications.

The Arduino Nicla Vision is available for around 95 EUR from the Arduino Store.

Installing dependencies

To set this device up in Edge Impulse, you will need to install the following software:

1. Edge Impulse CLI.

2. Arduino CLI.

   • Here's an instruction video for Windows.

   • The Arduino website has instructions for macOS and Linux.

3. On Linux:

   • GNU Screen: install for example via sudo apt install screen.

Connecting to Edge Impulse

There are two ways to connect the Nicla Vision to Edge Impulse:

• Using the official Edge Impulse firmware - it supports all onboard sensors, including camera.

• Using an ingestion script. This supports analog, IMU, proximity sensors and microphone (limited to 8 kHz), but not the camera. It is only recommended if you want to modify the ingestion flow for third-party sensors.

Edge Impulse firmware

1. Connect the development board to your computer

Use a micro-USB cable to connect the development board to your computer. Under normal circumstances, flash process should work without entering the bootloader manually. However if run into difficulties flashing the board, you can enter the bootloader by pressing RESET twice. The onboard LED should start pulsating to indicate this.

2. Update the firmware

The development board does not come with the right firmware yet. To update the firmware:

1. Download the latest Edge Impulse firmware, and unzip the file.

2. Open the flash script for your operating system (flash_windows.bat, flash_mac.command or flash_linux.sh) to flash the firmware.

3. Wait until flashing is complete, and press the RESET button once to launch the new firmware.

3. Setting keys

From a command prompt or terminal, run:

edge-impulse-daemon

This will start a wizard which will ask you to log in and choose an Edge Impulse project. If you want to switch projects run the command with --clean.

Alternatively, recent versions of Google Chrome and Microsoft Edge can collect data directly from your development board, without the need for the Edge Impulse CLI. See this blog post for more information.

Edge Impulse ingestion scripts

1. Connect the development board to your computer

Use a micro-USB cable to connect the development board to your computer.

2. Update the firmware

The development board does not come with the right firmware yet. To update the firmware:

1. Download the latest Edge Impulse ingestion sketches and unzip the file.

2. Open the nicla_vision_ingestion.ino (for IMU/proximity sensor) or nicla_vision_ingestion_mic.ino(for microphone) sketch in a text editor or the Arduino IDE.

3. For IMU/proximity sensor data ingestion into your Edge Impulse project, at the top of the file, select 1 or multiple sensors by un-commenting the defines and select the desired sample frequency (in Hz). For example, for the accelerometer sensor:

/**
 * @brief   Sample & upload data to Edge Impulse Studio.
 * @details Select 1 or multiple sensors by un-commenting the defines and select
 * a desired sample frequency. When this sketch runs, you can see raw sample
 * values outputted over the serial line. Now connect to the studio using the
 * `edge-impulse-data-forwarder` and start capturing data
 */
#define SAMPLE_ACCELEROMETER
//#define SAMPLE_GYROSCOPE
//#define SAMPLE_PROXIMITY

/**
 * Configure the sample frequency. This is the frequency used to send the data
 * to the studio regardless of the frequency used to sample the data from the
 * sensor. This differs per sensors, and can be modified in the API of the sensor
 */
#define FREQUENCY_HZ        10

For microphone data ingestion, you do not need to change the default parameters in the nicla_vision_ingestion_mic.ino sketch.

1. Then, from your sketch's directory, run the Arduino CLI to compile:

   Copy

   arduino-cli compile --fqbn arduino:mbed_nicla:nicla_vision --output-dir .

2. Then flash to your Nicla Vision using the Arduino CLI:

   Copy

   arduino-cli upload --fqbn arduino:mbed_nicla:nicla_vision

1. Wait until flashing is complete, and press the RESET button once to launch the new firmware.

3a. Data forwarder (Fusion sensors)

From a command prompt or terminal, run:

edge-impulse-data-forwarder

This will start a wizard which will ask you to log in, and choose an Edge Impulse project. You will also name your sensor's axes (depending on which sensor you selected in your compiled nicla_vision_ingestion.ino sketch). If you want to switch projects/sensors run the command with --clean. Please refer to the table below for the names used for each axis corresponding to the type of sensor:

Note: These exact axis names are required for the Edge Impulse Arduino library deployment example applications for the Nicla Vision.

3b. Data forwarder (Microphone)

From a command prompt or terminal, run:

edge-impulse-data-forwarder --baud 1000000 --frequency 8000

This will start a wizard which will ask you to log in and choose an Edge Impulse project. You will also name your sensor axes - in the case of the microphone, you need to enter audio. If you want to switch projects/sensors run the command with --clean.

Verifying that the device is connected

That's all! Your device is now connected to Edge Impulse. To verify this, go to your Edge Impulse project, and click Devices. The device will be listed here.

With everything set up you can now build your first machine learning model with these tutorials:

• Building a continuous motion recognition system.

• Recognizing sounds from audio.

• Responding to your voice

• Adding sight to your sensors

• Counting objects using FOMO

Looking to connect different sensors? Use the nicla_vision_ingestion.ino sketch and the Edge Impulse data forwarder to easily send data from any sensor on the Nicla Vision into your Edge Impulse project.

Deploying back to device

With the impulse designed, trained and verified you can deploy this model back to your Arduino Nicla Vision. This makes the model run without an internet connection, minimizes latency, and runs with minimum power consumption. Edge Impulse can package the complete impulse - including the signal processing code, neural network weights, and classification code - up into a single library that you can run on your development board.

Use the Running your impulse locally: On your Arduino tutorial and select one of the Nicla Vision examples.

The Infineon Semiconductor PSoC® 62S2 Wi-Fi® BLUETOOTH® Pioneer Kit (Cypress CY8CKIT-062S2) enables the evaluation and development of applications using the PSoC 62 Series MCU. This low-cost hardware platform enables the design and debug of the PSoC 62 MCU and the Murata 1LV Module (CYW43012 Wi-Fi + Bluetooth Combo Chip). The PSoC 6 MCU is Infineon' latest, ultra-low-power PSoC specifically designed for wearables and IoT products. The board features a PSoC 6 MCU, and a CYW43012 Wi-Fi/Bluetooth combo module. Infineon CYW43012 is a 28nm, ultra-low-power device that supports single-stream, dual-band IEEE 802.11n-compliant Wi-Fi MAC/baseband/radio and Bluetooth 5.0 BR/EDR/LE. When paired with the IoT sense expansion kit, the PSoC® 62S2 Wi-Fi® BLUETOOTH® Pioneer Kit can be used to easily interface a variety of sensors with the PSoC™ 6 MCU platform, specifically targeted for audio and machine learning applications which are fully supported by Edge Impulse! You'll be able to sample raw data as well as build and deploy trained machine learning models to your PSoC® 62S2 Wi-Fi® BLUETOOTH® Pioneer Kit, directly from the Edge Impulse Studio.

The Edge Impulse firmware for this development board is open source and hosted on GitHub: edgeimpulse/firmware-infineon-cy8ckit-062s2.

Installing dependencies

To set this device up with Edge Impulse, you will need to install the following software:

1. Infineon CyProgrammer. A utility program we will use to flash firmware images onto the target.

2. The Edge Impulse CLI which will enable you to connect your CY8CKIT-062S2 Pioneer Kit directly to Edge Impulse Studio, so that you can collect raw data and trigger in-system inferences.

Updating the firmware

Edge Impulse Studio can collect data directly from your CY8CKIT-062S2 Pioneer Kit and also help you trigger in-system inferences to debug your model, but in order to allow Edge Impulse Studio to interact with your CY8CKIT-062S2 Pioneer Kit you first need to flash it with our base firmware image.

1. Download the base firmware image

Download the latest Edge Impulse firmware, and unzip the file. Once downloaded, unzip it to obtain the firmware-infineon-cy8ckit-062s2.hex file, which we will be using in the following steps.

2. Connect the CY8CKIT-062S2 Pioneer Kit to your computer

Use a micro-USB cable to connect the CY8CKIT-062S2 Pioneer Kit to your development computer (where you downloaded and installed Infineon CyProgrammer).

3. Load the base firmware image with Infineon CyProgrammer

You can use Infineon CyProgrammer to flash your CY8CKIT-062S2 Pioneer Kit with our base firmware image. To do this, first select your board from the dropdown list on the top left corner. Make sure to select the item that starts with CY8CKIT-062S2-43012:

Then select the base firmware image file you downloaded in the first step above (i.e., the file named firmware-infineon-cy8ckit-062s2.hex). You can now press the Connect button to connect to the board, and finally the Program button to load the base firmware image onto the CY8CKIT-062S2 Pioneer Kit.

Connecting to Edge Impulse

With all the software in place, it's time to connect the CY8CKIT-062S2 Pioneer Kit to Edge Impulse.

1. Connect the development board to your computer

Use a micro-USB cable to connect the development board to your computer.

2. Setting keys

From a command prompt or terminal, run:

edge-impulse-daemon

This will start a wizard which will ask you to log in, and choose an Edge Impulse project. If you want to switch projects run the command with --clean.

Alternatively, recent versions of Google Chrome and Microsoft Edge can collect data directly from your development board, without the need for the Edge Impulse CLI. See this blog post for more information.

3. Verifying that the device is connected

That's all! Your device is now connected to Edge Impulse. To verify this, go to your Edge Impulse project, and click Devices on the left sidebar. The device will be listed there:

Next steps: Build a machine learning model

With everything set up you can now build your first machine learning model with these tutorials:

• Building a continuous motion recognition system.

• Recognizing sounds from audio.

• Responding to your voice.

Looking to connect different sensors? The Data forwarder lets you easily send data from any sensor into Edge Impulse.

Espressif ESP-EYE (ESP32) is a compact development board based on Espressif's ESP32 chip, equipped with a 2-Megapixel camera and a microphone. ESP-EYE also offers plenty of storage, with 8 MB PSRAM and 4 MB SPI flash - and it's fully supported by Edge Impulse. You'll be able to sample raw data, build models, and deploy trained machine learning models directly from the studio. It's available for around 22 USD from Mouser and a wide range of distributors.

There are plenty of other boards built with ESP32 chip - and of course there are custom designs utilizing ESP32 SoM. Edge Impulse firmware was tested with ESP-EYE and ESP FireBeetle boards, but there is a possibility to modify the firmware to use it with other ESP32 designs. Read more on that in Using with other boards section of this documentation.

The Edge Impulse firmware for this development board is open source and hosted on GitHub: edgeimpulse/firmware-espressif-esp32.

Installing dependencies

To set this device up in Edge Impulse, you will need to install the following software:

1. Edge Impulse CLI.

2. Python 3.

3. ESP Tool.

   • The ESP documentation website has instructions for macOS and Linux.

4. On Linux:

   • GNU Screen: install for example via sudo apt install screen.

Connecting to Edge Impulse

With all the software in place it's time to connect the development board to Edge Impulse.

1. Connect the development board to your computer

Use a micro-USB cable to connect the development board to your computer.

2. Update the firmware

The development board does not come with the right firmware yet. To update the firmware:

1. Download the latest Edge Impulse firmware, and unzip the file.

2. Open the flash script for your operating system (flash_windows.bat, flash_mac.command or flash_linux.sh) to flash the firmware.

3. Wait until flashing is complete.

3. Setting keys

From a command prompt or terminal, run:

edge-impulse-daemon

This will start a wizard which will ask you to log in, and choose an Edge Impulse project. If you want to switch projects run the command with --clean.

Alternatively, recent versions of Google Chrome and Microsoft Edge can collect data directly from your development board, without the need for the Edge Impulse CLI. See this blog post for more information.

4. Verifying that the device is connected

That's all! Your device is now connected to Edge Impulse. To verify this, go to your Edge Impulse project, and click Devices. The device will be listed here.

Next steps: building a machine learning model

With everything set up you can now build your first machine learning model with these tutorials:

• Building a continuous motion recognition system.

• Recognizing sounds from audio.

• Responding to your voice.

• Adding sight to your sensors.

• Counting objects using FOMO.

Looking to connect different sensors? The Data forwarder lets you easily send data from any sensor into Edge Impulse.

Sensors available

The standard firmware supports the following sensors:

• Camera: OV2640, OV3660, OV5640 modules from Omnivision

• Microphone: I2S microphone on ESP-EYE (MIC8-4X3-1P0)

• LIS3DHTR module connected to I2C (SCL pin 22, SDA pin 21)

• Any analog sensor, connected to A0

The analog sensor and LIS3DHTR module were tested on ESP32 FireBeetle board and Grove LIS3DHTR module.

Using with other ESP32 boards

ESP32 is a very popular chip both in a community projects and in industry, due to its high performance, low price and large amount of documentation/support available. There are other camera enabled development boards based on ESP32, which can use Edge Impulse firmware after applying certain changes, e.g.

• AI-Thinker ESP-CAM

• M5STACK ESP32 PSRAM Timer Camera X (OV3660)

• M5STACK ESP32 Camera Module Development Board (OV2640)

The pins used for camera connection on different development boards are not the same, therefore you will need to change the #define here to fit your development board, compile and flash the firmware. Specifically for AI-Thinker ESP-CAM, since this board needs an external USB to TTL Serial Cable to upload the code/communicate with the board, the data transfer baud rate must be changed to 115200 here.

The analog sensor and LIS3DH accelerometer can be used on any other development board without changes, as long as the interface pins are not changed. If I2C/ADC pins that accelerometer/analog sensor are connected to are different, from described in Sensors available section, you will need to change the values in LIS3DHTR component for ESP32, compile and flash it to your board.

Additionally, since Edge Impulse firmware is open-source and available to public, if you have made modifications/added new sensors capabilities, we encourage you to make a PR in firmware repository!

Deploy your impulse

To deploy your impulse on your ESP32 board, please see:

• Generate an Edge Impulse firmware (ESP32-EYE only)

• Download a C++ library (using ESP-IDF)

• Download an Arduino library

The Himax WE-I Plus is a tiny development board with a camera, a microphone, an accelerometer and a very fast DSP - and it's fully supported by Edge Impulse. You'll be able to sample raw data, build models, and deploy trained machine learning models directly from the studio. It's available for around 65 USD from Sparkfun.

The Edge Impulse firmware for this development board is open source and hosted on GitHub: edgeimpulse/firmware-himax-we-i-plus.

Installing dependencies

To set this device up in Edge Impulse, you will need to install the following software:

1. Edge Impulse CLI.

2. On Linux:

   • GNU Screen: install for example via sudo apt install screen.

Connecting to Edge Impulse

With all the software in place it's time to connect the development board to Edge Impulse.

1. Connect the development board to your computer

Use a micro-USB cable to connect the development board to your computer.

2. Update the firmware

The development board does not come with the right firmware yet. To update the firmware:

1. Download the latest Edge Impulse firmware, and unzip the file.

2. Open the flash script for your operating system (flash_windows.bat, flash_mac.command or flash_linux.sh) to flash the firmware.

3. Wait until flashing is complete, and press the RESET button once to launch the new firmware.

3. Setting keys

From a command prompt or terminal, run:

edge-impulse-daemon

This will start a wizard which will ask you to log in, and choose an Edge Impulse project. If you want to switch projects run the command with --clean.

Alternatively, recent versions of Google Chrome and Microsoft Edge can collect data directly from your development board, without the need for the Edge Impulse CLI. See this blog post for more information.

4. Verifying that the device is connected

That's all! Your device is now connected to Edge Impulse. To verify this, go to your Edge Impulse project, and click Devices. The device will be listed here.

Next steps: building a machine learning model

With everything set up you can now build your first machine learning model with these tutorials:

• Responding to your voice

• Recognize sounds from audio

• Adding sight to your sensors

• Object detection

• Counting objects using FOMO

Looking to connect different sensors? The Data forwarder lets you easily send data from any sensor into Edge Impulse.

Troubleshooting

All licenses are in use by other developers.

If you export to the Himax WE-I Plus you could receive the error: "All licenses are in use by other developers.". Unfortunately we have a limited number of licenses for the MetaWare compiler and these are shared between all Studio users. Try again in a little bit, or export your project as a C++ Library, add it to the edgeimpulse/firmware-himax-we-i-plus project and compile locally.

COM port not detected

If no device shows up in your OS (ie: COMxx, /dev/tty.usbxx) after connecting the board and your USB cable supports data transfer, you may need to install FTDI VCP driver.

The Portenta H7 is a powerful development board from Arduino with both a Cortex-M7 microcontroller and a Cortex-M4 microcontroller, a BLE/WiFi radio, and an extension slot to connect the Portenta vision shield - which adds a camera and dual microphones. At the moment the Portenta H7 is partially supported by Edge Impulse, letting you collect data from the camera, build computer vision models, and deploy trained machine learning models back to the development board. The Portenta H7 and the vision shield are available directly from Arduino for ~$150 in total.

There are two versions of the vision shield: one that has an Ethernet connection and one with a LoRa radio. Both of these can be used with Edge Impulse.

The Edge Impulse firmware for this development board is open source and hosted on GitHub: edgeimpulse/firmware-arduino-portenta-h7.

Installing dependencies

To set this device up in Edge Impulse, you will need to install the following software:

1. Edge Impulse CLI.

2. Arduino CLI.

   • Here's an instruction video for Windows.

   • The Arduino website has instructions for macOS and Linux.

3. On Linux:

   • GNU Screen: install for example via sudo apt install screen.

Connecting to Edge Impulse

With all the software in place it's time to connect the development board to Edge Impulse.

1. Connect the vision shield

Using the vision shield using two edge connectors on the back Portenta H7.

2. Connect the development board to your computer

Use a USB-C cable to connect the development board to your computer. Then, double-tap the RESET button to put the device into bootloader mode. You should see the green LED on the front pulsating.

3. Update the firmware

The development board does not come with the right firmware yet. To update the firmware:

1. Download the latest Edge Impulse firmware, and unzip the file.

2. Double press on the RESET button on your board to put it in the bootloader mode.

3. Open the flash script for your operating system (flash_windows.bat, flash_mac.command or flash_linux.sh) to flash the firmware.

4. Wait until flashing is complete, and press the RESET button once to launch the new firmware.

4. Setting keys

From a command prompt or terminal, run:

edge-impulse-daemon

This will start a wizard which will ask you to log in, and choose an Edge Impulse project. If you want to switch projects run the command with --clean.

Alternatively, recent versions of Google Chrome and Microsoft Edge can collect data directly from your development board, without the need for the Edge Impulse CLI. See this blog post for more information.

5. Verifying that the device is connected

That's all! Your device is now connected to Edge Impulse. To verify this, go to your Edge Impulse project, and click Devices. The device will be listed here.

Next steps: building a machine learning model

With everything set up you can now build your first machine learning model with these tutorials:

• Responding to your voice

• Recognize sounds from audio

• Adding sight to your sensors

• Object detection

• Counting objects using FOMO

Looking to connect different sensors? The Data forwarder lets you easily send data from any sensor into Edge Impulse.

Deploying back to device

• Download your custom firmware from the Deployment tab in the Studio and install the firmware with the same method as in the "Update the firmware" section and run the edge-impulse-run-impulse command:

Note that it may take up to 10 minutes to compile the firmware for the Arduino Portenta H7

• Use the Running your impulse locally: On your Arduino tutorial and select one of the portenta examples:

• For an end-to-end example that classifies data and then sends the result over LoRaWAN. Please see the example-portenta-lorawan example.

Troubleshooting

If you come across this issue:

Finding Arduino Mbed core...
arduino:mbed_portenta 2.6.1     2.6.1  Arduino Mbed OS Portenta Boards                                                  
Finding Arduino Mbed core OK
Finding Arduino Portenta H7...
Finding Arduino Portenta H7 OK at Arduino
dfu-util 0.10-dev

Copyright 2005-2009 Weston Schmidt, Harald Welte and OpenMoko Inc.
Copyright 2010-2021 Tormod Volden and Stefan Schmidt
This program is Free Software and has ABSOLUTELY NO WARRANTY
Please report bugs to http://sourceforge.net/p/dfu-util/tickets/

Warning: Invalid DFU suffix signature
A valid DFU suffix will be required in a future dfu-util release
No DFU capable USB device available
Error during Upload: uploading error: uploading error: exit status 74
Flashing failed. Here are some options:
If your error is 'incorrect FQBN' you'll need to upgrade the Arduino core via:
     $ arduino-cli core update-index
     $ arduino-cli core install arduino:mbed_portenta@2.6.1
Otherwise, double tap the RESET button to load the bootloader and try again
Press any key to continue . . .

You probably forgot to double press the RESET button before running the flash script.

The OpenMV Cam is a small and low-power development board with a Cortex-M7 microcontroller supporting MicroPython, a μSD card socket and a camera module capable of taking 5MP images - and it's fully supported by Edge Impulse. You'll be able to sample raw data, build models, and deploy trained machine learning models through the studio and the OpenMV IDE. It is available for 80 USD directly from .

Installing dependencies

To set this device up in Edge Impulse, you will need to install the following software:

Connecting to Edge Impulse

With all the software in place it's time to connect the development board to Edge Impulse. To make this easy we've put some tutorials together which takes you through all the steps to acquire data, train a model, and deploy this model back to your device.

The Nordic Semiconductor nRF5340 DK is a development board with dual Cortex-M33 microcontrollers, QSPI flash, and an integrated BLE radio - and it's fully supported by Edge Impulse. You'll be able to sample raw data, build models, and deploy trained machine learning models directly from the studio. As the nRF5340 DK does not have any built-in sensors we recommend you to pair this development board with the shield (with a MEMS accelerometer and a MEMS microphone). The nRF5340 DK is available for around 50 USD from a .

If you don't have the X-NUCLEO-IKS02A1 shield you can use the to capture data from any other sensor, and then follow the tutorial to run your impulse. Or, you can modify the example firmware (based on nRF Connect) to interact with other accelerometers or PDM microphones that are supported by Zephyr.

The Edge Impulse firmware for this development board is open source and hosted on GitHub: .

Installing dependencies

To set this device up in Edge Impulse, you will need to install the following software:

2. On Linux:

   • GNU Screen: install for example via sudo apt install screen.

Connecting to Edge Impulse

With all the software in place it's time to connect the development board to Edge Impulse.

1. Plugging in the X-NUCLEO-IKS02A1 MEMS expansion shield

Remove the pin header protectors on the nRF5340 DK and plug the X-NUCLEO-IKS02A1 shield into the development board.

Note: Make sure that the shield does not touch any of the pins in the middle of the development board. This might cause issues when flashing the board or running applications.

2. Connect the development board to your computer

Use a micro-USB cable to connect the development board to your computer. There are two USB ports on the development board, use the one on the short side of the board. Then, set the power switch to 'on'.

3. Update the firmware

The development board does not come with the right firmware yet. To update the firmware:

1. The development board is mounted as a USB mass-storage device (like a USB flash drive), with the name JLINK. Make sure you can see this drive.

3. Drag the nrf5340-dk.bin file to the JLINK drive.

4. Wait 20 seconds and press the BOOT/RESET button.

4. Setting keys

From a command prompt or terminal, run:

This starts a wizard which asks you to log in and choose an Edge Impulse project. If you want to switch projects run the command with --clean.

The nRF5340 DK exposes multiple UARTs. If prompted, choose the bottom one:

5. Verifying that the device is connected

Next steps: building a machine learning model

With everything set up you can now build your first machine learning model with these tutorials:

Troubleshooting

Failed to flash

If your board fails to flash new firmware (a FAIL.txt file might appear on the JLINK drive) you can also flash using nrfjprog.

2. Flash new firmware via:

The Nordic Semiconductor nRF9160 DK is a development board with an nRF9160 SIP incorporating a Cortex M-33 for your application, a full LTE-M/NB-IoT modem with GPS along with 1 MB of flash and 256 KB RAM. It also includes an nRF52840 board controller with Bluetooth Low Energy connectivity. The Development Kit is fully supported by Edge Impulse. You'll be able to sample raw data, build models, and deploy trained machine learning models directly from the studio. As the nRF9160 DK does not have any built-in sensors we recommend you to pair this development board with the shield (with a MEMS accelerometer and a MEMS microphone). The nRF9160 DK is available for around 150 USD from a variety of distributors including .

If you don't have the X-NUCLEO-IKS02A1 shield you can use the to capture data from any other sensor, and then follow the tutorial to run your impulse. Or, you can modify the example firmware (based on nRF Connect) to interact with other accelerometers or PDM microphones that are supported by Zephyr.

The Edge Impulse firmware for this development board is open source and hosted on GitHub: .

Installing dependencies

To set this device up in Edge Impulse, you will need to install the following software:

2. On Linux:

   • GNU Screen: install for example via sudo apt install screen.

Connecting to Edge Impulse

With all the software in place it's time to connect the development board to Edge Impulse.

1. Plugging in the X-NUCLEO-IKS02A1 MEMS expansion shield

Remove the pin header protectors on the nRF9160 DK and plug the X-NUCLEO-IKS02A1 shield into the development board.

Note: Make sure that the shield does not touch any of the pins in the middle of the development board. This might cause issues when flashing the board or running applications. You can also remove the shield before flashing the board.

2. Connect the development board to your computer

Use a micro-USB cable to connect the development board to your computer. There are two USB ports on the development board, use the one on the short side of the board. Then, set the power switch to 'on'.

3. Update the firmware

The development board does not come with the right firmware yet. To update the firmware:

1. The development board is mounted as a USB mass-storage device (like a USB flash drive), with the name JLINK. Make sure you can see this drive.

4. Flash the board controller, you only need to do this once. Go to step 4 if you've performed this step before.

   • Ensure that the PROG/DEBUG switch is in nRF52 position.

   • Copy board-controller.bin to the JLINK mass storage device.

1. Flash the application:

   • Ensure that the PROG/DEBUG switch is in nRF91 position.

   • Run the flash script for your Operating System.

2. Wait 20 seconds and press the BOOT/RESET button.

4. Setting keys

From a command prompt or terminal, run:

This starts a wizard which asks you to log in and choose an Edge Impulse project. If you want to switch projects run the command with --clean.

The nRF9160 DK exposes multiple UARTs. If prompted, choose the top one:

5. Verifying that the device is connected

Next steps: building a machine learning model

With everything set up you can now build your first machine learning model with these tutorials:

The Nordic Semiconductor Thingy:91 is an easy-to-use battery-operated prototyping platform for cellular IoT using LTE-M, NB-IoT and GPS. It is ideal for creating Proof-of-Concept (PoC), demos and initial prototypes in your cIoT development phase. Thingy:91 is built around the and is certified for a broad range of LTE bands globally, meaning the Nordic Thingy:91 can be used just about anywhere in the world. There is an multiprotocol SoC on the Thingy:91. This offers the option of adding Bluetooth Low Energy connectivity to your project.

Nordic's Thingy:91 is fully supported by Edge Impulse. You'll be able to sample raw data, build models, and deploy trained machine learning models directly from the studio. Thingy:91 is available for around 120 USD from a .

The Edge Impulse firmware for this development board is open source and hosted on GitHub: .

Installing dependencies

To set this device up in Edge Impulse, you will need to install the following software:

3. On Linux:

   • GNU Screen: install for example via sudo apt install screen.

Updating the firmware

Before you start a new project, you need to update the Thingy:91 firmware to our latest build.

1. Connect the development board to your computer

Use a micro-USB cable to connect the development board to your computer. Then, set the power switch to 'on'.

2. Download the firmware

1. firmware.hex: the Edge Impulse firmware image for the nRF9160 SoC, and

2. connectivity-bridge.hex: a connectivity application for the nRF52840 that you only need on older boards (hardware version < 1.4)

3. Update the firmware

1. Open nRF Connect for Desktop and launch the Programmer application.

2. Scroll down in the menu on the right and make sure Enable MCUboot is selected.

1. Switch off the Nordic Thingy:91.

2. Press the multi-function button (SW3) while switching SW1 to the ON position.

1. In the Programmer navigation bar, click Select device.

2. In the menu on the right, click Add HEX file > Browse, and select the firmware.hex file from the firmware previously downloaded.

3. Scroll down in the menu on the right to Device and click Write:

1. In the MCUboot DFU window, click Write. When the update is complete, a Completed successfully message appears.

2. You can now disconnect the board.

Connecting to Edge Impulse

With all the software in place it's time to connect the development board to Edge Impulse. From a command prompt or terminal, run:

This starts a wizard which asks you to log in and choose an Edge Impulse project. If you want to switch projects run the command with --clean.

The Thingy:91 exposes multiple UARTs. If prompted, choose the first one:

4. Verifying that the device is connected

Next steps: building a machine learning model

With everything set up you can now build your first machine learning model with this tutorial:

The Nordic Semiconductor nRF52840 DK is a development board with a Cortex-M4 microcontroller, QSPI flash, and an integrated BLE radio - and it's fully supported by Edge Impulse. You'll be able to sample raw data, build models, and deploy trained machine learning models directly from the studio. As the nRF52840 DK does not have any built-in sensors we recommend you to pair this development board with the shield (with a MEMS accelerometer and a MEMS microphone). The nRF52840 DK is available for around 50 USD from a variety of distributors including .

If you don't have the X-NUCLEO-IKS02A1 shield you can use the to capture data from any other sensor, and then follow the tutorial to run your impulse. Or, you can modify the example firmware (based on nRF Connect) to interact with other accelerometers or PDM microphones that are supported by Zephyr.

The Edge Impulse firmware for this development board is open source and hosted on GitHub: .

Installing dependencies

To set this device up in Edge Impulse, you will need to install the following software:

2. On Linux:

   • GNU Screen: install for example via sudo apt install screen.

Connecting to Edge Impulse

With all the software in place it's time to connect the development board to Edge Impulse.

1. Plugging in the X-NUCLEO-IKS02A1 MEMS expansion shield

Remove the pin header protectors on the nRF52840 DK and plug the X-NUCLEO-IKS02A1 shield into the development board.

Note: Make sure that the shield does not touch any of the pins in the middle of the development board. This might cause issues when flashing the board or running applications.

2. Connect the development board to your computer

Use a micro-USB cable to connect the development board to your computer. There are two USB ports on the development board, use the one on the short side of the board. Then, set the power switch to 'on'.

3. Update the firmware

The development board does not come with the right firmware yet. To update the firmware:

1. The development board is mounted as a USB mass-storage device (like a USB flash drive), with the name JLINK. Make sure you can see this drive.
3. Drag the nrf52840-dk.bin file to the JLINK drive.

4. Wait 20 seconds and press the BOOT/RESET button.

4. Setting keys

From a command prompt or terminal, run:

This will start a wizard which will ask you to log in and choose an Edge Impulse project. If you want to switch projects run the command with --clean.

5. Verifying that the device is connected

Next steps: building a machine learning model

With everything set up you can now build your first machine learning model with these tutorials:

Troubleshooting

No JLINK drive

If you don't see the JLINK drive show up when you connect your nRF52840 DK you'll have to update the interface firmware.

1. Set the power switch to 'off'.

2. Hold BOOT/RESET while you set the power switch to 'on'.

3. Your development board should be mounted as BOOTLOADER.

5. After 20 seconds disconnect the USB cable, and plug the cable back in.

6. The development board should now be mounted as JLINK.

Failed to flash

If your board fails to flash new firmware (a FAIL.txt file might appear on the JLINK drive) you can also flash using nrfjprog.

2. Flash new firmware via:

The Nordic Thingy:53™ is an easy-to-use prototyping platform, it makes it possible to create prototypes and proof-of-concepts without the need to build custom hardware. Thingy:53 is built around the . The capacity of its dual Arm Cortex-M33 processors enables it to do embedded machine learning (ML), both collecting data and running trained ML models on the device. The Bluetooth Low Energy radio allows it to connect to smart phones, tablets, laptops and similar devices, without the need for a wired connection. Other protocols like Thread, Zigbee and proprietary 2.4 GHz protocols are also supported by the radio. It also includes a well of different integrated sensors, an NFC antenna, and has two buttons and one RGB LED that simplifies input and output.

Nordic's Thingy:53 is fully supported by Edge Impulse and every Thingy:53 is shipped with already flashed. You'll be able to sample raw data, build models, and deploy trained machine learning models directly out-of-the-box via the Edge Impulse Studio or the Nordic nRF Edge Impulse and apps over BLE connection. The Thingy:53 is available for around 120 USD from a .

The Edge Impulse firmware for this development board is open source and hosted on GitHub: .

Installing dependencies

To set this device up in Edge Impulse via USB serial or external debug probe, you will need to install the following software:

3. On Linux:

   • GNU Screen: install for example via sudo apt install screen.

Updating the firmware

1. Connect the development board to your computer

Use a USB cable to connect the development board to your computer. Then, set the power switch to 'on'.

2. Download the firmware

Download the latest Edge Impulse firmware:

• • *-full.zip contains HEX files to upgrade the device through the external probe.

• • *-dfu.zip contains dfu_application.zip package to upgrade the already flashed device through the Serial/USB bootloader.

3. Update the firmware

Connecting to Edge Impulse

Through nRF Edge Impulse mobile app

Through serial connection

With all the software in place it's time to connect the development board to Edge Impulse. From a command prompt or terminal, run:

This starts a wizard which asks you to log in and choose an Edge Impulse project. If you want to switch projects run the command with --clean.

If prompted to select a device, choose ZEPHYR:

4. Verifying that the device is connected

Next steps: building a machine learning model

With everything set up you can now build your first machine learning model with this tutorial:

Connecting to the nRF Edge Impulse mobile application

Now that you have created an Edge Impulse account and trained your first Edge Impulse machine learning model, using the Nordic nRF Edge Impulse app you can deploy your impulse to your Nordic Thingy:53 and acquire/upload new sensor data into your Edge Impulse projects.

Installation & Login

Connect or remove a device

Select the Devices tab to connect to your Thingy:53 device to your mobile phone:

To remove your connected Thingy:53 from your project, select the connected device name and scroll to the bottom of the device page to remove it.

Data Acquisition

To view existing data samples in your Edge Impulse project, select the Data Acquisition tab. To record and upload a new data sample into your project, click on the "+" button at the top right of the app. Select your sensor, type in the sample label, and choose a sample length and frequency, then select Start Sampling.

Deployment

Build and deploy your Edge Impulse model to your Thingy:53 via the Deployment tab. Select your project from the top drop-down, select your connected Thingy:53 device, and click Build:

The app will start building your project and uploading the firmware to the connected Thingy:53:

Inferencing

Select the Inferencing tab to view the inferencing results of the model flashed to the connected Thingy:53:

Settings

Select the Settings tab to view your logged-in account information, BLE scanner settings, and application version. Click on your account name to view your Edge Impulse projects and logout of your account.

Troubleshooting

Lost BLE connection to device

• Reconnect your device by selecting your device name on the Devices tab and click "Reconnect".

• Make sure power cables are plugged in properly.

• Do not use iPhone/Android app multitasking during data acquisition, firmware deployment, or inferencing tasks, as the BLE streaming connection will be closed.

The Renesas CK-RA6M5, Cloud Kit for RA6M5 MCU Group, enables users to experience the cloud connectivity options available from Renesas and Renesas Partners. A broad array of sensors on the CK-RA6M5 provide multiple options for observing user interaction with the Cloud Kit. By selecting from a choice of add-on devices, multiple cloud connectivity options are available.

The Edge Impulse firmware for this development board is open source and hosted on GitHub: edgeimpulse/firmware-renesas-ck-ra6m5.

Installing dependencies

To set this device up in Edge Impulse, you will need to install the following software:

1. Edge Impulse CLI.

2. On Linux:

   • GNU Screen: install for example via sudo apt install screen.

Updating the firmware

Edge Impulse Studio can collect data directly from your CK-RA6M5 Cloud Kit and also help you trigger in-system inferences to debug your model, but in order to allow Edge Impulse Studio to interact with your CK-RA6M5 Cloud Kit you first need to flash it with our base firmware image.

1. Download the base firmware image

Download the latest Edge Impulse firmware, and unzip the file, then locate the flash-script folder included, which we will be using in the following steps.

2. Connect the CK-RA6M5 Cloud Kit to your computer

1. Check that:

   • J22 is set to link pins 2-3

   • J21 link is closed

   • J16 Link is open

2. Connect J14 and J20 on the CK-RA6M5 board to USB ports on the host PC using the 2 micro USB cables supplied.

3. Power LED (LED6) on the CK-RA6M5 board lights up white, indicating that the CK-RA6M5 board is powered on.

3. Load the base firmware image

Open the flash script for your operating system (flash_windows.bat, flash_mac.command or flash_linux.sh) to flash the firmware.

Connecting to Edge Impulse

1. Setting keys

From a command prompt or terminal, run:

edge-impulse-daemon

This will start a wizard which will ask you to log in, and choose an Edge Impulse project. If you want to switch projects run the command with --clean.

Alternatively, recent versions of Google Chrome and Microsoft Edge can collect data directly from your development board, without the need for the Edge Impulse CLI. See this blog post for more information.

2. Verifying that the device is connected

That's all! Your device is now connected to Edge Impulse. To verify this, go to your Edge Impulse project, and click Devices on the left sidebar. The device will be listed there:

Next steps: Build a machine learning model

With everything set up you can now build your first machine learning model with these tutorials:

• Building a continuous motion recognition system.

• Recognizing sounds from audio.

• Responding to your voice.

Looking to connect different sensors? The Data forwarder lets you easily send data from any sensor into Edge Impulse.

The Silicon Labs Thunderboard Sense 2 is a complete development board with a Cortex-M4 microcontroller, a wide variety of sensors, a microphone, Bluetooth Low Energy and a battery holder - and it's fully supported by Edge Impulse. You'll be able to sample raw data, build models, and deploy trained machine learning models directly from the studio - and even stream your machine learning results over BLE to a phone. It's available for around 20 USD directly from Silicon Labs.

The Edge Impulse firmware for this development board is open source and hosted on on GitHub: edgeimpulse/firmware-silabs-thunderboard-sense-2.

Installing dependencies

To set this device up in Edge Impulse, you will need to install the following software:

1. Edge Impulse CLI.

2. On Linux:

   • GNU Screen: install for example via sudo apt install screen.

Connecting to Edge Impulse

With all the software in place it's time to connect the development board to Edge Impulse.

1. Connect the development board to your computer

Use a micro-USB cable to connect the development board to your computer. The development board should mount as a USB mass-storage device (like a USB flash drive), with the name TB004. Make sure you can see this drive.

2. Update the firmware

The development board does not come with the right firmware yet. To update the firmware:

1. Download the latest Edge Impulse firmware.

2. Drag the silabs-thunderboard-sense2.bin file to the TB004 drive.

3. Wait 30 seconds.

3. Setting keys

From a command prompt or terminal, run:

edge-impulse-daemon

This will start a wizard which will ask you to log in and choose an Edge Impulse project. If you want to switch projects run the command with --clean.

Alternatively, recent versions of Google Chrome and Microsoft Edge can collect data directly from your development board, without the need for the Edge Impulse CLI. See this blog post for more information.

4. Verifying that the device is connected

That's all! Your device is now connected to Edge Impulse. To verify this, go to your Edge Impulse project, and click Devices. The device will be listed here.

Next steps: building a machine learning model

With everything set up you can now build your first machine learning model with these tutorials:

• Building a continuous motion recognition system.

• Recognizing sounds from audio.

• Responding to your voice.

Looking to connect different sensors? The Data forwarder lets you easily send data from any sensor into Edge Impulse.

Did you know? You can also stream the results of your impulse over BLE to a nearby phone or gateway: see Streaming results over BLE to your phone.

Troubleshooting

Dragging and dropping Edge Impulse .bin file results in FAIL.TXT

When dragging and dropping an Edge Impulse pre-built .bin firmware file, the binary seems to flash, but when the device reconnects a FAIL.TXT file appears with the contents "Error while connecting to CPU" and the following errors appear from the Edge Impulse CLI impulse runner:

$ edge-impulse-run-impulse
Edge Impulse impulse runner v1.12.5
[SER] Connecting to /dev/tty.usbmodem0004401612721
[SER] Serial is connected, trying to read config...
[SER] Failed to get info off device:undefined. Is this device running a binary built through Edge Impulse? Reconnecting in 5 seconds...
[SER] Serial is connected, trying to read config...

To fix this error, install the Simplicity Studio 5 IDE and flash the binary through the IDE's built in "Upload application..." menu under "Debug Adapters", and select your Edge Impulse firmware to flash:

Your Edge Impulse inferencing application should then run successfully with edge-impulse-run-impulse.

The Silicon Labs xG24 Dev Kit (xG24-DK2601B) is a compact, feature-packed development platform built for the EFR32MG24 Cortex-M33 microcontroller. It provides the fastest path to develop and prototype wireless IoT products. This development platform supports up to +10 dBm output power and includes support for the 20-bit ADC as well as the xG24's AI/ML hardware accelerator. The platform also features a wide variety of sensors, a microphone, Bluetooth Low Energy and a battery holder - and it's fully supported by Edge Impulse! You'll be able to sample raw data as well as build and deploy trained machine learning models directly from the Edge Impulse Studio - and even stream your machine learning results over BLE to a phone.

The Edge Impulse firmware for this development board is open source and hosted on GitHub: edgeimpulse/firmware-silabs-xg24.

Installing dependencies

To set this device up with Edge Impulse, you will need to install the following software:

1. Simplicity Commander. A utility program we will use to flash firmware images onto the target.

2. The Edge Impulse CLI which will enable you to connect your xG24 Dev Kit directly to Edge Impulse Studio, so that you can collect raw data and trigger in-system inferences.

Updating the firmware

Edge Impulse Studio can collect data directly from your xG24 Dev Kit and also help you trigger in-system inferences to debug your model, but in order to allow Edge Impulse Studio to interact with your xG24 Dev Kit you first need to flash it with our base firmware image.

1. Download the base firmware image

Download the latest Edge Impulse firmware, and unzip the file. Once downloaded, unzip it to obtain the firmware-xg24.hex file, which we will be using in the following steps.

2. Connect the xG24 Dev Kit to your computer

Use a micro-USB cable to connect the xG24 Dev Kit to your development computer (where you downloaded and installed Simplicity Commander).

3. Load the base firmware image with Simplicity Commander

You can use Simplicity Commander to flash your xG24 Dev Kit with our base firmware image. To do this, first select your board from the dropdown list on the top left corner:

Then go to the "Flash" section on the left sidebar, and select the base firmware image file you downloaded in the first step above (i.e., the file named firmware-xg24.hex). You can now press the Flash button to load the base firmware image onto the xG24 Dev Kit.

Connecting to Edge Impulse

With all the software in place, it's time to connect the xG24 Dev Kit to Edge Impulse.

1. Connect the development board to your computer

Use a micro-USB cable to connect the development board to your computer.

2. Setting keys

From a command prompt or terminal, run:

edge-impulse-daemon

This will start a wizard which will ask you to log in, and choose an Edge Impulse project. If you want to switch projects run the command with --clean.

Alternatively, recent versions of Google Chrome and Microsoft Edge can collect data directly from your development board, without the need for the Edge Impulse CLI. See this blog post for more information.

3. Verifying that the device is connected

That's all! Your device is now connected to Edge Impulse. To verify this, go to your Edge Impulse project, and click Devices on the left sidebar. The device will be listed there:

Next steps: Build a machine learning model

With everything set up you can now build your first machine learning model with these tutorials:

• Object detection on the SiLabs xG24 Dev Kit.

• Building a continuous motion recognition system.

• Recognizing sounds from audio.

• Responding to your voice.

Looking to connect different sensors? The Data forwarder lets you easily send data from any sensor into Edge Impulse.

Grove - Vision AI Module is a thumb-sized board based on Himax HX6537-A processor which is equipped with a 2-Megapixel OV2640 camera, microphone, 3-axis accelerometer and 3-axis gyroscope. It offers storage with 32 MB SPI flash, comes pre-installed with ML algorithms for face recognition and people detection and supports customized models as well. It is compatible with the XIAO ecosystem and Arduino, all of which makes it perfect for getting started with AI-powered camera projects!

It is fully supported by Edge Impulse which means you will be able to sample raw data from the camera, build models, and deploy trained machine learning models to the module directly from the studio without any programming required. Grove - Vision AI Module is available for purchase directly from Seeed Studio Bazaar.

Quick links access:

• Firmware source code: GitHub repository

• Pre-compiled firmware: seeed-grove-vision-ai.zip

Installing dependencies

To set this board up in Edge Impulse, you will need to install the following software:

1. Edge Impulse CLI.

2. On Linux:

   • GNU Screen: install for example via sudo apt install screen.

3. Download the latest Bouffalo Lab Dev Cube

Connecting to Edge Impulse

With all the software in place it's time to connect the board to Edge Impulse.

1. Update BL702 chip firmware

BL702 is the USB-UART chip which enables the communication between the PC and the Himax chip. You need to update this firmware in order for the Edge Impulse firmware to work properly.

1. Download BL702-firmware-grove-vision-ai.zip and extract it to obtain tinyuf2-grove_vision_ai.bin file

2. Connect the board to the PC via a USB Type-C cable while holding down the Boot button on the board
3. Open previously installed Bouffalo Lab Dev Cube software, select BL702/704/706, and then click Finish
4. Go to MCU tab. Under Image file, click Browse and select the firmware you just downloaded.
5. Click Refresh, choose the Port related to the connected board, set Chip Erase to True, click Open UART, click Create & Download and wait for the process to be completed .

You will see the output as All Success if it went well.

Note: If the flashing throws an error, try to click Create & Download multiple times until you see the All Success message.

2. Update Edge Impulse firmware

The board does not come with the right Edge Impulse firmware yet. To update the firmware:

1. Download the latest Edge Impulse firmware and extract it to obtain firmware.uf2 file

2. Connect the board again to the PC via USB Type-C cable and double-click the Boot button on the board to enter mass storage mode

3. After this you will see a new storage drive shown on your file explorer as GROVEAI. Drag and drop the firmware.uf2 file to GROVEAI drive

Once the copying is finished GROVEAI drive will disappear. This is how we can check whether the copying is successful or not.

3. Setting keys

From a command prompt or terminal, run:

edge-impulse-daemon

This will start a wizard which will ask you to log in, and choose an Edge Impulse project. If you want to switch projects run the command with --clean.

Alternatively, recent versions of Google Chrome and Microsoft Edge can collect data directly from your board, without the need for the Edge Impulse CLI. See this blog post for more information.

4. Verifying that the device is connected

That's all! Your device is now connected to Edge Impulse. To verify this, go to your Edge Impulse project, and click Devices. The device will be listed here.

Collecting data from Grove - Vision AI

With everything set up you can now build and run your first machine learning model with these tutorials:

• Adding sight to your sensors.

• Object detection.

• Counting objects using FOMO.

Looking to connect different sensors? The Data forwarder lets you easily send data from any sensor into Edge Impulse.

Deploy model to Grove - Vision AI

After building the machine learning model and downloading the Edge Impulse firmware from Edge Impulse Studio, deploy the model uf2 to Grove - Vision AI by following steps 1 and 2 under Update Edge Impulse firmware section.

Compile Edge Impulse firmware from source

If you want to compile the Edge Impulse firmware from the source code, you can visit this GitHub repo and follow the instructions included in the README.

The model used for the official firmware can be found in this public project.

Sony's Spresense is a small, but powerful development board with a 6 core Cortex-M4F microcontroller and integrated GPS, and a wide variety of add-on modules including an extension board with headphone jack, SD card slot and microphone pins, a camera board, a sensor board with accelerometer, pressure, and geomagnetism sensors, and Wi-Fi board - and it's fully supported by Edge Impulse. You'll be able to sample raw data, build models, and deploy trained machine learning models directly from the studio.

To get started with the Sony Spresense and Edge Impulse you'll need:

• The Spresense main development board - available for around 55 USD from a wide range of distributors.

• The Spresense extension board - to connect external sensors.

• A micro-SD card to store samples.

In addition you'll want some sensors, these ones are fully supported (note that you can collect data from any sensor on the Spresense with the data forwarder):

• For image models: the Spresense CXD5602PWBCAM1 camera add-on.

• For accelerometer models: the Spresense Sensor EVK-70 add-on.

• For audio models: an electret microphone and a 2.2K Ohm resistor, wired to the extension board's audio channel A, following this schema (picture here).

  • Note: for audio models you must also have a FAT formatted SD card for the extension board, with the Spresense's DSP files included in a BIN folder on the card, see instructions here and a screenshot of the SD card directory here.

The Edge Impulse firmware for this development board is open source and hosted on GitHub: edgeimpulse/firmware-sony-spresense.

Installing dependencies

To set this device up in Edge Impulse, you will need to install the following software:

1. Edge Impulse CLI.

2. On Linux:

   • GNU Screen: install for example via sudo apt install screen.

Connecting to Edge Impulse

With all the software in place it's time to connect the development board to Edge Impulse.

1. Connect the optional camera, sensor, extension board, Wi-Fi add-ons, and SD card

Make sure the SD card is formatted as FAT before inserting it into the Spresense.

2. Connect the development board to your computer

Use a micro-USB cable to connect the main development board (not the extension board) to your computer.

3. Update the bootloader and the firmware

The development board does not come with the right firmware yet. To update the firmware:

1. Install Python 3.7 or higher.

2. Download the latest Edge Impulse firmware, and unzip the file.

3. Open the flash script for your operating system (flash_windows.bat, flash_mac.command or flash_linux.sh) to flash the firmware.

4. Wait until flashing is complete. The on-board LEDs should stop blinking to indicate that the new firmware is running.

4. Setting keys

From a command prompt or terminal, run:

edge-impulse-daemon

This will start a wizard which will ask you to log in and choose an Edge Impulse project. If you want to switch projects run the command with --clean.

Alternatively, recent versions of Google Chrome and Microsoft Edge can collect data directly from your development board, without the need for the Edge Impulse CLI. See this blog post for more information.

5. Verifying that the device is connected

That's all! Your device is now connected to Edge Impulse. To verify this, go to your Edge Impulse project, and click Devices. The device will be listed here.

Next steps: building a machine learning model

With everything set up you can now build your first machine learning model with these tutorials:

• Responding to your voice

• Recognize sounds from audio

• Adding sight to your sensors

• Object detection

• Counting objects using FOMO

Looking to connect different sensors? The Data forwarder lets you easily send data from any sensor into Edge Impulse.

Troubleshooting

Error when flashing

If you see:

ValueError: dlsym(RTLD_DEFAULT, kIOMasterPortDefault): symbol not found

Upgrade pyserial:

pip3 install --upgrade pyserial

Daemon does not start

If the edge-impulse-daemon or edge-impulse-run-impulse commands do not start it might be because of an error interacting with the SD card or because your board has an old version of the bootloader. To see the debug logs, run:

edge-impulse-run-impulse --raw

And press the RESET button on the board. If you see Welcome to nash you'll need to update the bootloader. To do so:

1. Install and launch the Arduino IDE.

2. Go to Preferences and under 'Additional Boards Manager URLs' add https://github.com/sonydevworld/spresense-arduino-compatible/releases/download/generic/package_spresense_index.json (if there's already text in this text box, add a , before adding the new URL).

3. Then go to Tools > Boards > Board manager, search for 'Spresense' and click Install.

4. Select the right board via: Tools > Boards > Spresense boards > Spresense.

5. Select your serial port via: Tools > Port and selecting the serial port for the Spresense board.

6. Select the Spresense programmer via: Tools > Programmer > Spresense firmware updater.

7. Update the bootloader via Tools > Burn bootloader.

Then update the firmware again (from step 3: Update the bootloader and the firmware).

Seeed SenseCAP A1101 - LoraWAN Vision AI Sensor is an image recognition AI sensor designed for developers. SenseCAP A1101 - LoRaWAN Vision AI Sensor combines TinyML AI technology and LoRaWAN long-range transmission to enable a low-power, high-performance AI device solution for both indoor and outdoor use.

This sensor features Himax high-performance, low-power AI vision solution which supports the Google TensorFlow Lite framework and multiple TinyML AI platforms.

It is fully supported by Edge Impulse which means you will be able to sample raw data from the camera, build models, and deploy trained machine learning models to the module directly from the studio without any programming required. SenseCAP - Vision AI Module is available for purchase directly from Seeed Studio Bazaar.

Installing dependencies

To set A1101 up in Edge Impulse, you will need to install the following software:

1. Edge Impulse CLI.

2. On Linux:

   • GNU Screen: install for example via sudo apt install screen.

3. Download the latest Bouffalo Lab Dev Cube-All-Platform

Connecting to Edge Impulse

With all the software in place, it's time to connect the A1101 to Edge Impulse.

1. Update BL702 chip firmware

BL702 is the USB-UART chip which enables the communication between the PC and the Himax chip. You need to update this firmware in order for the Edge Impulse firmware to work properly.

1. Get the latest bootloader firmware (tinyuf2-sensecap_vision_ai_xxx.bin.)

2. Connect the A1101 to the PC via a USB Type-C cable while holding down the Boot button on the A1101.
3. Open previously installed Bouffalo Lab Dev Cube software, select BL702/704/706, and then click Finish
4. Go to the MCU tab. Under Image file, click Browse and select the firmware you just downloaded.
5. Click Refresh, choose the Port related to the connected A1101, set Chip Erase to True, click Open UART, click Create & Download and wait for the process to be completed .

You will see the output as All Success if it went well.

2. Update Edge Impulse firmware

A1101 does not come with the right Edge Impulse firmware yet. To update the firmware:

1. Download the latest Edge Impulse firmware and extract it to obtain firmware.uf2 file

2. Connect the A1101 again to the PC via USB Type-C cable and double-click the Boot button on the A1101 to enter mass storage mode

3. After this you will see a new storage drive shown on your file explorer as SENSECAP. Drag and drop the firmware.uf2 file to SENSECAP drive

Once the copying is finished SENSECAP drive will disappear. This is how we can check whether the copying is successful or not.

3. Setting keys

From a command prompt or terminal, run:

edge-impulse-daemon

This will start a wizard which will ask you to log in, and choose an Edge Impulse project. If you want to switch projects run the command with --clean.

Alternatively, recent versions of Google Chrome and Microsoft Edge can collect data directly from your A1101, without the need for the Edge Impulse CLI. See this blog post for more information.

4. Verifying that the device is connected

That's all! Your device is now connected to Edge Impulse. To verify this, go to your Edge Impulse project, and click Devices. The device will be listed here.

Device connected to Edge Impulse correctly!

Next steps: Build a machine learning model

With everything set up, you can now build and run your first machine learning model with these tutorials:

• Object detection.

• Counting objects using FOMO.

Looking to connect different sensors? The Data forwarder lets you easily send data from any sensor into Edge Impulse.

Collecting data from Seeed SenseCAP A1101

Frames from the onboard camera can be directly captured from the studio:

Finally, once a model is trained, it can be easily deployed to the A1101 – Vision AI Module to start inferencing!

Deploy model to SenseCAP A1101

After building the machine learning model and downloading the Edge Impulse firmware from Edge Impulse Studio, deploy the model uf2 to SenseCAP - Vision AI by following steps 1 and 2 under Update Edge Impulse firmware

Drag and drop the firmware.uf2 file from EDGE IMPULSE to SENSECAP drive.

When you run this on your local interface:

edge-impulse-daemon --debug

it will ask you to click a URL, then you will see a live preview of the camera on your device.

Compile Edge Impulse firmware from source

If you want to compile the Edge Impulse firmware from the source code, you can visit this GitHub repo and follow the instructions included in the README.

The model used for the official firmware can be found in this public project.

Connect to the LoraWAN® Network

In addition to connecting directly to a computer to view real-time detection data, you can also transmit these data through LoraWAN® and finally upload them to the SenseCAP cloud platform or a third-party cloud platform. On the SenseCAP cloud platform, you can view the data in a cycle and display it graphically through your mobile phone or computer. The SenseCAP cloud platform and SenseCAP Mate App use the same account system.

Since our focus here is on describing the model training process, we won't go into the details of the cloud platform data display. But if you're interested, you can always visit the SenseCAP cloud platform to try adding devices and viewing data. It's a great way to get a better understanding of the platform's capabilities!

You can get more information on how to use SenseCAP A1101 here

How to Select a LoRaWAN Gateway

LoRaWAN® network coverage is required when using sensors, there are two options.

Seeed provides:

• SenseCAP M2 for Helium network

• SenseCAP M2 Multi-Platform for standard LoraWAN® network

If you have interests, please kindly click for more details.

Configure your model on the SenseCap Mate

1. Download SenseCAP Mate

   • Android

   • iOS

2. Open SenseCAP Mate and login
3. Under Config screen, select Vision AI Sensor
4. Press and hold the configuration button on the SenseCap A1101 for 3 seconds to enter bluetooth pairing mode
5. Click Setup and it will start scanning for nearby SenseCAP A1101 devices- Go to Settings and make sure Object Detection and User Defined 1 is selected. If not, select it and click Send
6. Go to General and click Detect, you'll see the actual data here.
7. Click here to open a preview window of the camera stream
8. Click Connect button. Then you will see a pop up on the browser. Select SenseCAP Vision AI - Paired and click Connect
9. View real-time inference results using the preview window!

The cats are detected with bounding boxes around them. Here "0" corresponds to each detection of the same class. If you have multiple classes, they will be named as 0, 1, 2, 3, 4 and so on. Also the confidence score for each detected object (0.72 in above demo) is displayed!

The Synaptics Katana KA10000 board is a low-power AI evaluation kit from Synaptics that has the KA10000 AI Neural Network processor onboard. The evaluation kit is provided with a separate Himax HM01B0 QVGA monochrome camera module and 2 onboard zero power Vesper microphones. The board has an embedded STLIS2Dw12 accelerometer and an optional TI OPT3001 ambient light sensor. The connectivity to the board is provided with an IEEE 802.11n ultra low power WiFi module that is integrated with a Bluetooth 5.x, in addition to 4 Peripheral Modules (PMOD) connectors to provide I2C. UART, GPIO, I2S/SPI interfaces.

The package contains several accessories:

• The Himax image sensor.

• The PMOD-I2C USB firmware configuration board.

• The PMOD-UART USB adapter.

• 2 AAA batteries

• Enclosure.

The Edge Impulse firmware for this board is open source and hosted on GitHub: .

Installing dependencies

To set this device up in Edge Impulse, you will need to install the following software:

2. On Linux:

   • GNU Screen: install for example via sudo apt install screen.

Connecting to Edge Impulse

1. Connect the development board to your computer

In order to update the firmware, it is necessary to use the PMOD-I2C USB firmware configuration board. The PMOD-I2C board is connected to the Katana board on the north right PMOD-I2C interface (as shown in the image at the top of this page), then you need to use a USB C cable to connect the firmware configuration board to the host PC.

In addition to the PMOD-I2C configuration board. You need to connect the PMOD-UART extension to the Katana board which is located on the left side of the board. Then you need to use a Micro-USB cable to connect the board to your computer.

2. Update the firmware

The board is shipped originally with a sound detection firmware by default. You can upload new firmware to the flash memory by following these instructions:

• Verify that you have correctly connected the firmware configuration board.

• Run the flash script for your operating system (flash_windows.bat, flash_mac.command or flash_linux.sh) to flash the firmware.

• Wait until flashing is complete.

3. Setting keys

From a command prompt or terminal, run:

This will start a wizard which will ask you to log in, and choose an Edge Impulse project. If you want to switch projects run the command with --clean.

4. Verifying that the device is connected

Next steps: building a machine learning model

With everything set up you can now build your first machine learning model with these tutorials, and board-specific public projects:

The ST IoT Discovery Kit (also known as the B-L475E-IOT01A) is a development board with a Cortex-M4 microcontroller, MEMS motion sensors, a microphone and WiFi - and it's fully supported by Edge Impulse. You'll be able to sample raw data, build models, and deploy trained machine learning models directly from the studio. It's available for around 50 USD from a variety of distributors including .

The Edge Impulse firmware for this development board is open source and hosted on GitHub: .

Installing dependencies

To set this device up in Edge Impulse, you will need to install the following software:

2. On Windows:
3. On Linux:

   • GNU Screen: install for example via sudo apt install screen.

Connecting to Edge Impulse

With all the software in place it's time to connect the development board to Edge Impulse.

1. Connect the development board to your computer

Use a micro-USB cable to connect the development board to your computer. There are two USB ports on the development board, use the one the furthest from the buttons.

2. Update the firmware

The development board does not come with the right firmware yet. To update the firmware:

1. The development board is mounted as a USB mass-storage device (like a USB flash drive), with the name DIS_L4IOT. Make sure you can see this drive.

3. Drag the DISCO-L475VG-IOT01A.bin file to the DIS_L4IOT drive.

4. Wait until the LED stops flashing red and green.

3. Setting keys and WiFi credentials

From a command prompt or terminal, run:

This will start a wizard which will ask you to log in, choose an Edge Impulse project, and set up your WiFi network. If you want to switch projects run the command with --clean.

4. Verifying that the device is connected

Next steps: building a machine learning model

With everything set up you can now build your first machine learning model with these tutorials:

Troubleshooting

Unable to set up WiFi with ST B-L475E-IOT01A development board

If you experience the following error when attempting to connect to a WiFi network:

My device is not responding, and nothing happens when I attempt to update the firmware

If the LED does not flash red and green when you copy the .bin file to the device and instead is a solid red color, and you are unable to connect the device with Edge Impulse, there may be an issue with your device's native firmware.

To restore functionality, use the following tool from ST to update your board to the latest version:

I don't see the DIS_L4IOT drive, or cannot connect over serial to the board (Linux)

You might need to set up udev rules on Linux before being able to talk to the device. Create a file named /etc/udev/rules.d/50-stlink.rules and add the following content:

Then unplug the development board and plug it back in.

The TinyML Board is a with a microphone and accelerometer, USB host microcontroller and an always-on Neural Decision Processor™, featuring ultra low-power consumption, a fully connected neural network architecture, and fully supported by Edge Impulse. You'll be able to sample raw data, build models, and deploy trained embedded machine learning models directly from the Edge Impulse studio to create the next generation of low-power, high-performance audio interfaces.

The Edge Impulse firmware for this development board is open source and hosted on .

Installing dependencies

To set this device up in Edge Impulse, you will need to install the following software:

Connecting to Edge Impulse

1. Download the firmware

Select one of the 2 firmwares below for audio or IMU projects:

Insert SD Card if you need IMU data acquisition and connect the USB cable to your computer. Double-click on the script for your OS. The script will flash the Arduino firmware and a default model on the NDP101 chip.

2. Connect the development board to your computer

Connect the Syntiant TinyML Board directly to your computer's USB port. Linux, Mac OS, and Windows 10 platforms are supported.