LogoLogo
HomeAPI & SDKsProjectsForumStudio
  • Getting started
    • For beginners
    • For ML practitioners
    • For embedded engineers
  • Frequently asked questions (FAQ)
  • Tutorials
    • End-to-end tutorials
      • Computer vision
        • Image classification
        • Object detection
          • Object detection with bounding boxes
          • Detect objects with centroid (FOMO)
        • Visual anomaly detection
        • Visual regression
      • Audio
        • Sound recognition
        • Keyword spotting
      • Time-series
        • Motion recognition + anomaly detection
        • Regression + anomaly detection
        • HR/HRV
        • Environmental (Sensor fusion)
    • Data
      • Data ingestion
        • Collecting image data from the Studio
        • Collecting image data with your mobile phone
        • Collecting image data with the OpenMV Cam H7 Plus
        • Using the Edge Impulse Python SDK to upload and download data
        • Trigger connected board data sampling
        • Ingest multi-labeled data using the API
      • Synthetic data
        • Generate audio datasets using Eleven Labs
        • Generate image datasets using Dall-E
        • Generate keyword spotting datasets using Google TTS
        • Generate physics simulation datasets using PyBullet
        • Generate timeseries data with MATLAB
      • Labeling
        • Label audio data using your existing models
        • Label image data using GPT-4o
      • Edge Impulse Datasets
    • Feature extraction
      • Building custom processing blocks
      • Sensor fusion using embeddings
    • Machine learning
      • Classification with multiple 2D input features
      • Visualize neural networks decisions with Grad-CAM
      • Sensor fusion using embeddings
      • FOMO self-attention
    • Inferencing & post-processing
      • Count objects using FOMO
      • Continuous audio sampling
      • Multi-impulse (C++)
      • Multi-impulse (Python)
    • Lifecycle management
      • CI/CD with GitHub Actions
      • Data aquisition from S3 object store - Golioth on AI
      • OTA model updates
        • with Arduino IDE (for ESP32)
        • with Arduino IoT Cloud
        • with Blues Wireless
        • with Docker on Allxon
        • with Docker on Balena
        • with Docker on NVIDIA Jetson
        • with Espressif IDF
        • with Nordic Thingy53 and the Edge Impulse app
        • with Particle Workbench
        • with Zephyr on Golioth
    • API examples
      • Customize the EON Tuner
      • Ingest multi-labeled data using the API
      • Python API bindings example
      • Running jobs using the API
      • Trigger connected board data sampling
    • Python SDK examples
      • Using the Edge Impulse Python SDK to run EON Tuner
      • Using the Edge Impulse Python SDK to upload and download data
      • Using the Edge Impulse Python SDK with Hugging Face
      • Using the Edge Impulse Python SDK with SageMaker Studio
      • Using the Edge Impulse Python SDK with TensorFlow and Keras
      • Using the Edge Impulse Python SDK with Weights & Biases
    • Expert network projects
  • Edge Impulse Studio
    • Organization hub
      • Users
      • Data campaigns
      • Data
        • Cloud data storage
      • Data pipelines
      • Data transformation
        • Transformation blocks
      • Upload portals
      • Custom blocks
        • Custom AI labeling blocks
        • Custom deployment blocks
        • Custom learning blocks
        • Custom processing blocks
        • Custom synthetic data blocks
        • Custom transformation blocks
      • Health reference design
        • Synchronizing clinical data with a bucket
        • Validating clinical data
        • Querying clinical data
        • Transforming clinical data
    • Project dashboard
      • Select AI hardware
    • Devices
    • Data acquisition
      • Uploader
      • Data explorer
      • Data sources
      • Synthetic data
      • Labeling queue
      • AI labeling
      • CSV Wizard (time-series)
      • Multi-label (time-series)
      • Tabular data (pre-processed & non-time-series)
      • Metadata
      • Auto-labeler | deprecated
    • Impulses
    • EON Tuner
      • Search space
    • Processing blocks
      • Audio MFCC
      • Audio MFE
      • Audio Syntiant
      • Flatten
      • HR/HRV features
      • Image
      • IMU Syntiant
      • Raw data
      • Spectral features
      • Spectrogram
      • Custom processing blocks
      • Feature explorer
    • Learning blocks
      • Anomaly detection (GMM)
      • Anomaly detection (K-means)
      • Classification
      • Classical ML
      • Object detection
        • MobileNetV2 SSD FPN
        • FOMO: Object detection for constrained devices
      • Object tracking
      • Regression
      • Transfer learning (images)
      • Transfer learning (keyword spotting)
      • Visual anomaly detection (FOMO-AD)
      • Custom learning blocks
      • Expert mode
      • NVIDIA TAO | deprecated
    • Retrain model
    • Live classification
    • Model testing
    • Performance calibration
    • Deployment
      • EON Compiler
      • Custom deployment blocks
    • Versioning
    • Bring your own model (BYOM)
    • File specifications
      • deployment-metadata.json
      • ei-metadata.json
      • ids.json
      • parameters.json
      • sample_id_details.json
      • train_input.json
  • Tools
    • API and SDK references
    • Edge Impulse CLI
      • Installation
      • Serial daemon
      • Uploader
      • Data forwarder
      • Impulse runner
      • Blocks
      • Himax flash tool
    • Edge Impulse for Linux
      • Linux Node.js SDK
      • Linux Go SDK
      • Linux C++ SDK
      • Linux Python SDK
      • Flex delegates
      • Rust Library
    • Rust Library
    • Edge Impulse Python SDK
  • Run inference
    • C++ library
      • As a generic C++ library
      • On Android
      • On your desktop computer
      • On your Alif Ensemble Series Device
      • On your Espressif ESP-EYE (ESP32) development board
      • On your Himax WE-I Plus
      • On your Raspberry Pi Pico (RP2040) development board
      • On your SiLabs Thunderboard Sense 2
      • On your Spresense by Sony development board
      • On your Syntiant TinyML Board
      • On your TI LaunchPad using GCC and the SimpleLink SDK
      • On your Zephyr-based Nordic Semiconductor development board
    • Arm Keil MDK CMSIS-PACK
    • Arduino library
      • Arduino IDE 1.18
    • Cube.MX CMSIS-PACK
    • Docker container
    • DRP-AI library
      • DRP-AI on your Renesas development board
      • DRP-AI TVM i8 on Renesas RZ/V2H
    • IAR library
    • Linux EIM executable
    • OpenMV
    • Particle library
    • Qualcomm IM SDK GStreamer
    • WebAssembly
      • Through WebAssembly (Node.js)
      • Through WebAssembly (browser)
    • Edge Impulse firmwares
    • Hardware specific tutorials
      • Image classification - Sony Spresense
      • Audio event detection with Particle boards
      • Motion recognition - Particle - Photon 2 & Boron
      • Motion recognition - RASynBoard
      • Motion recognition - Syntiant
      • Object detection - SiLabs xG24 Dev Kit
      • Sound recognition - TI LaunchXL
      • Keyword spotting - TI LaunchXL
      • Keyword spotting - Syntiant - RC Commands
      • Running NVIDIA TAO models on the Renesas RA8D1
      • Two cameras, two models - running multiple object detection models on the RZ/V2L
  • Edge AI Hardware
    • Overview
    • Production-ready
      • Advantech ICAM-540
      • Seeed SenseCAP A1101
      • Industry reference design - BrickML
    • MCU
      • Ambiq Apollo4 family of SoCs
      • Ambiq Apollo510
      • Arducam Pico4ML TinyML Dev Kit
      • Arduino Nano 33 BLE Sense
      • Arduino Nicla Sense ME
      • Arduino Nicla Vision
      • Arduino Portenta H7
      • Blues Wireless Swan
      • Espressif ESP-EYE
      • Himax WE-I Plus
      • Infineon CY8CKIT-062-BLE Pioneer Kit
      • Infineon CY8CKIT-062S2 Pioneer Kit
      • Nordic Semi nRF52840 DK
      • Nordic Semi nRF5340 DK
      • Nordic Semi nRF9160 DK
      • Nordic Semi nRF9161 DK
      • Nordic Semi nRF9151 DK
      • Nordic Semi nRF7002 DK
      • Nordic Semi Thingy:53
      • Nordic Semi Thingy:91
      • Open MV Cam H7 Plus
      • Particle Photon 2
      • Particle Boron
      • RAKwireless WisBlock
      • Raspberry Pi RP2040
      • Renesas CK-RA6M5 Cloud Kit
      • Renesas EK-RA8D1
      • Seeed Wio Terminal
      • Seeed XIAO nRF52840 Sense
      • Seeed XIAO ESP32 S3 Sense
      • SiLabs Thunderboard Sense 2
      • Sony's Spresense
      • ST B-L475E-IOT01A
      • TI CC1352P Launchpad
    • MCU + AI accelerators
      • Alif Ensemble
      • Arduino Nicla Voice
      • Avnet RASynBoard
      • Seeed Grove - Vision AI Module
      • Seeed Grove Vision AI Module V2 (WiseEye2)
      • Himax WiseEye2 Module and ISM Devboard
      • SiLabs xG24 Dev Kit
      • STMicroelectronics STM32N6570-DK
      • Synaptics Katana EVK
      • Syntiant Tiny ML Board
    • CPU
      • macOS
      • Linux x86_64
      • Raspberry Pi 4
      • Raspberry Pi 5
      • Texas Instruments SK-AM62
      • Microchip SAMA7G54
      • Renesas RZ/G2L
    • CPU + AI accelerators
      • AVNET RZBoard V2L
      • BrainChip AKD1000
      • i.MX 8M Plus EVK
      • Digi ConnectCore 93 Development Kit
      • MemryX MX3
      • MistyWest MistySOM RZ/V2L
      • Qualcomm Dragonwing RB3 Gen 2 Dev Kit
      • Renesas RZ/V2L
      • Renesas RZ/V2H
      • IMDT RZ/V2H
      • Texas Instruments SK-TDA4VM
      • Texas Instruments SK-AM62A-LP
      • Texas Instruments SK-AM68A
      • Thundercomm Rubik Pi 3
    • GPU
      • Advantech ICAM-540
      • NVIDIA Jetson
      • Seeed reComputer Jetson
    • Mobile phone
    • Porting guide
  • Integrations
    • Arduino Machine Learning Tools
    • AWS IoT Greengrass
    • Embedded IDEs - Open-CMSIS
    • NVIDIA Omniverse
    • Scailable
    • Weights & Biases
  • Tips & Tricks
    • Combining impulses
    • Increasing model performance
    • Optimizing compute time
    • Inference performance metrics
  • Concepts
    • Glossary
    • Course: Edge AI Fundamentals
      • Introduction to edge AI
      • What is edge computing?
      • What is machine learning (ML)?
      • What is edge AI?
      • How to choose an edge AI device
      • Edge AI lifecycle
      • What is edge MLOps?
      • What is Edge Impulse?
      • Case study: Izoelektro smart grid monitoring
      • Test and certification
    • Data engineering
      • Audio feature extraction
      • Motion feature extraction
    • Machine learning
      • Data augmentation
      • Evaluation metrics
      • Neural networks
        • Layers
        • Activation functions
        • Loss functions
        • Optimizers
          • Learned optimizer (VeLO)
        • Epochs
    • What is embedded ML, anyway?
    • What is edge machine learning (edge ML)?
Powered by GitBook
On this page
  • Prerequisites
  • Cloning the base repository
  • Deploying your impulse
  • Running the impulse
  • Seeing the output

Was this helpful?

Export as PDF
  1. Run inference
  2. C++ library

On your Zephyr-based Nordic Semiconductor development board

PreviousOn your TI LaunchPad using GCC and the SimpleLink SDKNextArm Keil MDK CMSIS-PACK

Last updated 2 months ago

Was this helpful?

Impulses can be deployed as a C++ library. This packages all your signal processing blocks, configuration and learning blocks up into a single package. You can include this package in your own application to run the impulse locally. In this tutorial you'll export an impulse, and build a Zephyr RTOS application for the nRF52840 DK / nRF5340 DK / nRF9160DK / Thingy:91 development board to classify sensor data.

A working Zephyr RTOS build environment is required

This tutorial assumes that you're already familiar with building applications for the or other Zephyr RTOS supported board, and that you have your environment set up to compile applications for this platform. For this tutorial, you can use the or higher.

Prerequisites

Make sure you followed the tutorial, and have a trained impulse. Also, make sure you have a working Zephyr build environment, including the following tools:

  • Either the which includes Zephyr and all its dependencies (v1.6.0 or higher), or a .

  • The .

  • Optional: The and . These command line tools are required if you use the to upload firmware to your target board.

Cloning the base repository

We created an example repository which contains a small application that compliments the tutorial. This application can take raw, hard-coded inputs as an argument, and print out the final classification to the serial port so it can be read from your development computer. You can either or import the repository using Git:

git clone https://github.com/edgeimpulse/example-standalone-inferencing-zephyr.git

Fully featured open source repos are also available

If you are looking for sample projects showcasing all sensors and features supported by Edge Impulse out of the box, we have public firmware repos available for the Nordic Semiconductor nRF52840, nRF5340 and nRF9160 development kits as well as for the Thingy:91 and Thingy:53. See

Deploying your impulse

Head over to your Edge Impulse project, and go to the Deployment page. From here you can obtain a packaged library containing the Edge Impulse C++ SDK, your impulse, and all required external dependencies. Select C++ library and click Build to create the library.

Download the .zip file and extract the contents. Now copy the following directories to the 'example-standalone-inferencing-zephyr' folder (which you downloaded above).

  • edge-impulse-sdk

  • model-parameters

  • tflite-model

Your final folder structure should look like this:

 example-standalone-inferencing-zephyr
 ├── CMakeLists.txt
 ├── edge-impulse-sdk
 ├── model-parameters
 ├── prj.conf
 ├── README.md
 ├── sample.yaml
 ├── src
 ├── tflite-model
 └── utils

Running the impulse

To verify that the Zephyr application performs the same classification when running locally on your board, we need to use the same raw inputs as those provided to the Live classification for any given timestamp. To do so, click on the 'Copy to clipboard' button next to 'Raw features'. This will copy the raw values from this validation file, before any signal processing or inferencing happened.

Next, open src/main.cpp in the example directory and paste the raw features inside the static const float features[] definition. For example:

static const float features[] = {
    -19.8800, -0.6900, 8.2300, -17.6600, -1.1300, 5.9700, ...
};

And use west or your usual method to build the application:

# nRF52840 DK
$ west build -b nrf52840dk_nrf52840

# nRF5340 DK
$ west build -b nrf5340dk_nrf5340_cpuapp

# nRF9160DK
$ west build -b nrf9160dk_nrf9160ns

# Thingy:91
$ west build -b thingy91_nrf9160

Invalid choice: 'build'

If you try to build the application but it throws an 'invalid choice' error like:

$ west build -b nrf52840dk_nrf52840
usage: west [-h] [-z ZEPHYR_BASE] [-v] [-V] <command> ...
west: error: argument <command>: invalid choice: 'build' (choose from 'init', 'update', 'list', 'manifest', 'diff', 'status', 'forall', 'help', 'config', 'topdir', 'selfupdate')
$ zephyr\zephyr-env.cmd

On macOS / Linux

$ source zephyr/zephyr-env.sh

If you have set up the Segger J-LINK tools and the board that comes with J-LINK debug probe, you can also flash this application with:

$ west flash

otherwise if your board shows up as a mass storage device, you can find the build/zephyr/zephyr.bin file and drag it to the JLINK USB mass-storage device in the same way you do with a USB flash drive.

Boards such as Thingy:91 and Thingy:53 do not come with in built J-LINK debug probe, and cannot be used with west flash directly. They do include connector that enable users to connect with external J-LINK debug probe and take advantage of west flash command.

Seeing the output

To see the output of the impulse, connect to the development board over a serial port on baud rate 115,200 and reset the board. You can do this with your favourite serial monitor or with the Edge Impulse CLI:

$ edge-impulse-run-impulse --raw

This will show you the output of the signal processing pipeline and the results of the classification:

Edge Impulse standalone inferencing (Zephyr)
Running neural network...
Predictions (time: 1 ms.):
idle:   0.015319
snake:  0.000444
updown: 0.006182
wave:   0.978056
Anomaly score (time: 0 ms.): 0.133557
Predictions (DSP: 18 ms., Classification: 1 ms., Anomaly: 0 ms.): 
[0.01532, 0.00044, 0.00618, 0.97806, 0.134]

The output should match the values you just saw in the studio. If it does, you now have your impulse running on your Zephyr development board!

Connecting live sensors?

With the project ready it's time to verify that the application works. Head back to the studio and click on Live classification in the project you created for the tutorial, then load a testing sample, and click on a row under 'Detailed result'.

You'll need to set up your environment variables correctly (). You can do so by opening a command prompt or terminal window and running the commands below from the zephyr parent directory: On Windows

For the nRF9160DK, you also have to make sure the at least once.

Now that you have verified that the impulse works with hard-coded inputs, you should be ready to plug live sensors from your board. A demonstration on how to plug sensor values into the classifier can be found here: .

nRF52840DK
nRF Connect SDK v1.6.0
Continuous motion recognition
nRF Connect SDK
manual installation of the Zephyr build environment
GNU ARM Embedded Toolchain (version 9-2019-q4-major)
nRF comand line tools
Segger J-Link tools
west command line interface
Continuous motion recognition
download the application
edgeimpulse/firmware-nordic-nrf52840dk-nrf5340dk
edgeimpulse/firmware-nordic-nrf9160dk
edgeimpulse/firmware-nordic-thingy91
edgeimpulse/firmware-nordic-thingy53
continuous motion recognition
more info
Data forwarder - classifying data (Zephyr)
Selecting the row with timestamp '320' under 'Detailed result'.
Copying the raw features.
board controller has been flashed