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
  • Download the Arduino Library
  • Test your impulse the "static buffer" example
  • Running the impulse
  • Seeing the output
  • Connecting sensors?
  • Boards manager
  • Troubleshooting
  • Error: 'ei_run_impulse' was not declared in this scope
  • Multiple libraries were found for ...
  • No such file or directory: include <arm_math.h>
  • macro "min" passed 3 arguments, but takes just 2
  • error: 'va_start' was not declared in this scope
  • Empty array when printing results
  • Slow DSP operations
  • Code compiling fails under Windows OS
  • Failed to connect to COM3 (Arduino Portenta H7)
  • No DFU capable USB device available (Arduino Portenta H7)
  • Nicla sensors don't match the sensors required in the model (Nicla Sense ME)

Was this helpful?

Export as PDF
  1. Run inference

Arduino library

PreviousArm Keil MDK CMSIS-PACKNextArduino IDE 1.18

Last updated 3 months ago

Was this helpful?

Impulses can be deployed as an Arduino library. This packages all of your signal processing blocks, configuration and learning blocks up into a single package. You can include this package in your own sketches to run the impulse locally. In this tutorial, you'll export an impulse, and integrate the impulse in a sketch to classify sensor data.

This tutorial should work on most Arm-based Arduino development boards with at least 64K of RAM, like the , and the .

In October 2022, we also added support for . It has been tested with the ESP-EYE and the ESP32-CAM AI Thinker.

ESP32 compatibility

ESP32 sketches are tested with 2.0.4 ESP32 Arduino Core https://github.com/espressif/arduino-esp32/releases/tag/2.0.4

For the Arduino Nicla Voice, refer to the .

Navigate further down this page, on the section to see how to setup Edge Impulse-compatible boards.

Knowledge required

This tutorial assumes that you're familiar with the Arduino IDE, and that you're comfortable building Arduino sketches. If you're unfamiliar with these tools you can build binaries directly for your development board from the Deployment page in the studio.

Prerequisites

Make sure you followed one of the following tutorials, and have a trained impulse:

  • .

  • .

  • .

  • (only for boards with built-in IMU sensor).

  • .

Also, install the following software:

Download the Arduino Library

Head over to your Edge Impulse project, and go to Deployment.

From here you can create the full library which contains the impulse and all external required libraries. Select Arduino library and click Build to create the library. This will download the arduino-compatible .zip file:

Test your impulse the "static buffer" example

Before starting to write code with your custom logic, make sure your impulse runs as expected on your board. This will ensure that you can compile the generated Arduino library containing your impulse and that the inference results are correct.

To do so, add the library and open an example, open the Arduino IDE and:

  1. Choose Sketch > Include Library > Add .ZIP library....

  2. Select the ZIP file, and then click the Choose button.

  3. Then, load an example by going to File > Examples > Your project name - Edge Impulse > static_buffer > static_buffer.

    In some configurations, it can be needed to restart Arduino IDE to see the examples.

  4. Voila. You now have an example application that loads your impulse.

Running the impulse

With the project ready it's time to verify that the application works. Head back to the studio and click on Live classification. Then load a validation sample, and click on a row under 'Detailed result'.

To verify that the local application classifies the same, we need the raw features for this 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.

In the sketch 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, ...
};

For example:

Then, select the port that your board is connected to in the Arduino IDE menu: Tools > Port > your Arduino board.

Then click Upload in the Arduino IDE to build and flash the application.

Seeing the output

To see the output of the impulse, open the serial monitor from the Arduino IDE via Tools > Serial monitor, and selecting baud rate 115,200.

This will run the signal processing pipeline, and then classify the output:

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

Which matches the values we just saw in the studio. You now have your impulse running on your Arduino development board!

Connecting sensors?

Boards manager

We also provide examples for all the officially supported targets that include sampling the raw features from the onboard sensors.

To use them, make sure to install the right development board under Tools->Boards->Boards Manager. We officially support:

  • Arduino Nano 33 BLE Sense

    For the Arduino Nano 33 BLE Sense, install the following board:

  • Arduino Portenta H7

    For the Arduino Portenta H7, make sure to install the Arduino Mbed OS Portenta Boards v2.8.0 and to select the Arduino Portenta H7 (M7 core) board and the Flash Split 2 MB M7 + M4 in SDRAM:

  • Arduino Nicla boards

    For the Arduino Nicla Vision and the Arduino Nicla Sense ME, install the following board:

  • Espressif ESP32

    For the ESP32 boards, we officially support the ESP-EYE. Other boards have been tested such as the ESP32-CAM AI Thinker. To install ESP32 boards, go to Arduino->Preferences and add the following link to the additional boards manager URLs: https://raw.githubusercontent.com/espressif/arduino-esp32/gh-pages/package_esp32_index.json. Then install the ESP32 boards from the board manager menu.

    Make sure to enable PSRAM if you need to run image-based models (image classification or FOMO).

    The following settings should work with the ESP-EYE and the ESP32-CAM AI-Thinker:

    • Board Selection: ESP32 Dev Module

    • PSRAM: Enabled

    • Upload Speed: 115200

    By default, the esp32-camera sketch is configured to work with the ESP-EYE. To change the camera model replace:

    #define CAMERA_MODEL_ESP_EYE // Has PSRAM
    //#define CAMERA_MODEL_AI_THINKER // Has PSRAM

    by

    //#define CAMERA_MODEL_ESP_EYE // Has PSRAM
    #define CAMERA_MODEL_AI_THINKER // Has PSRAM

Troubleshooting

Error: 'ei_run_impulse' was not declared in this scope

If you see the following error:

error: either all initializer clauses should be designated or none of them should be
1789 | .channels = input->dims->data[3]
// These sketches are tested with 2.0.4 ESP32 Arduino Core
// https://github.com/espressif/arduino-esp32/releases/tag/2.0.4

When using Arduino-ESP32 core v3.x, you may encounter a compilation error related to initializer clauses. To fix this, ensure that either all initializer values in data_dims_t structs are designated or none of them are. For details, refer to the fixes in conv.cpp and depthwise_conv.cpp within the model code.

Multiple libraries were found for ...

Exported libraries are automatically versioned, but it's possible that the Arduino IDE gets confused on which version to use leading to an error like: Multiple libraries were found for .... You can delete old versions of the libraries to mitigate this. The libraries are located at:

  • Windows: My Documents > Arduino > libraries

  • macOS: ~/Documents/Arduino/libraries/

  • Linux: ~/sketchbook/libraries

No such file or directory: include <arm_math.h>

~/Documents/Arduino/libraries/ei-accelerometer-impulse/src/edge-impulse-sdk/CMSIS/NN/Source/PoolingFunctions/arm_max_pool_s8.c:32:10: fatal error: arm_math.h: No such file or directory
 #include <arm_math.h>

Some users have indicated that this issue can be solved by reinstalling the Arduino IDE.

macro "min" passed 3 arguments, but takes just 2

error: 'va_start' was not declared in this scope

This error can be seen while compiling for a SAMD21-based target. You will need to add the standard library in your sketch:

#include <cstdarg>

Empty array when printing results

If the predictions are not properly printed, e.g.:

Edge Impulse standalone inferencing (Arduino)
run_classifier returned: 0
Predictions (DSP: 369 ms., Classification: 3 ms., Anomaly: 4 ms.): 
[, , , , ]

Then your printing library does not support printing floating point numbers (this happens for example on the Arduino MKR WAN 1300). You can get around this by converting the predictions to integers. E.g.:

    ei_printf("Predictions (DSP: %d ms., Classification: %d ms., Anomaly: %d ms.): \n",
        result.timing.dsp, result.timing.classification, result.timing.anomaly);

    // print the predictions
    ei_printf("[");
    for (size_t ix = 0; ix < EI_CLASSIFIER_LABEL_COUNT; ix++) {
        ei_printf("%d", static_cast<int>(result.classification[ix].value * 100));
#if EI_CLASSIFIER_HAS_ANOMALY == 1
        ei_printf(", ");
#else
        if (ix != EI_CLASSIFIER_LABEL_COUNT - 1) {
            ei_printf(", ");
        }
#endif
    }
#if EI_CLASSIFIER_HAS_ANOMALY == 1
    ei_printf("%d", static_cast<int>(result.anomaly));
#endif
    ei_printf("]\n");

Slow DSP operations

Where possible the signal processing code utilizes the vector extensions on your platform, but these are not enabled on all platforms. If these are not enabled we fall back to a software implementation which is slower. We don't enable these on all platforms because the wide variety of platform and core versions Arduino supports, but you can see enable them for your platform by adding the following code on the first line of your sketch, before any includes (only works on Arm cores):

#define EIDSP_USE_CMSIS_DSP		        1
#define EIDSP_LOAD_CMSIS_DSP_SOURCES    1

If this still does not work you need to edit the src/edge-impulse-sdk/dsp/config.hpp file in the library and add, before #ifndef EIDSP_USE_CMSIS_DSP:

#define EIDSP_USE_CMSIS_DSP		        1
#define EIDSP_LOAD_CMSIS_DSP_SOURCES    1

If you have a target that ships on old versions of CMSIS Core (like the SAMD21 targets) you might need to also declare some new macros, e.g.:

#define __STATIC_FORCEINLINE                   __attribute__((always_inline)) static inline
#define __SSAT(ARG1, ARG2) \
__extension__ \
({                          \
  int32_t __RES, __ARG1 = (ARG1); \
  __ASM volatile ("ssat %0, %1, %2" : "=r" (__RES) :  "I" (ARG2), "r" (__ARG1) : "cc" ); \
  __RES; \
 })

Code compiling fails under Windows OS

fork/exec C:\Users\MYUSER\AppData\Local\Arduino15\packages\arduino\tools\arm-none-eabi-gcc\7-2017q4/bin/arm-none-eabi-g++.exe: The filename or extension is too long.

This error is usually thrown when the list of object files to compile exceeds Windows max number of characters (32k) in a command line.

Note: This issue is reportedly fixed in Arduino CLI v0.14 and in the Arduino IDE 1.8.15 and above.

If updating your Arduino IDE does not work you can overcome this issue by downloading the platform.local.txt below for your target:

Failed to connect to COM3 (Arduino Portenta H7)

[SER] Connecting to COM3
[SER] Failed to connect to COM3 retrying in 5 seconds Opening COM3: Access denied
[SER] You might need `sudo` or set up the right udev rules
[SER] Failed to connect to COM3 retrying in 5 seconds Opening COM3: Access denied
[SER] You might need `sudo` or set up the right udev rules
[SER] Failed to connect to COM3 retrying in 5 seconds Opening COM3: Access denied
[SER] You might need `sudo` or set up the right udev rules

Make sure the vision shield is present.

No DFU capable USB device available (Arduino Portenta H7)

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 need to put the board in its bootloader mode. Double-press and the RESET button before flashing the board.

Nicla sensors don't match the sensors required in the model (Nicla Sense ME)

Starting inferencing in 2 seconds...
ERR: Nicla sensors don't match the sensors required in the model
Following sensors are required: accel.x + accel.y + accel.z + gyro.x + gyro.y + gyro.z + ori.heading + ori.pitch + ori.roll + rotation.x ...

It means that the axis names are different than the ones provided in the default example. To fix it, you can modify the eiSensors nicla_sensors[] (near line 70) in the sketch example to add your custom names. e.g.:

/** Used sensors value function connected to label name */
eiSensors nicla_sensors[] =
{
    "X", &get_accX,
    "Y", &get_accY,
    "Z", &get_accZ,
    "gyrX", &get_gyrX,
    "gyrY", &get_gyrY,
    "gyrZ", &get_gyrZ,
    "heading", &get_oriHeading,
    "pitch", &get_oriPitch,
    "roll", &get_oriRoll,
    "rotX", &get_rotX,
    "rotY", &get_rotY,
    "rotZ", &get_rotZ,
    "rotW", &get_rotW,
    "temperature", &get_temperature,
    "barometer", &get_barrometric_pressure,
    "humidity", &get_humidity,
    "gas", &get_gas,
};

.

A demonstration on how to plug sensor values into the classifier can be found here: .

the ,

the ,

the ,

the

the .

You can find other camera model pin definitions .

You may be using an incompatible version of the ESP32 Core. Please revert to 2.0.4 this is also noted in our

see the following forum post for more information

If you're compiling on a SAMD21-based target, you'll see the above error. This is a in the Arduino core for the SAMD21. If you apply the the issue will go away.

. Copy this file under the Arduino mbed directory, i.e: C:\Users\MYUSER\AppData\Local\Arduino15\packages\arduino\hardware\mbed\1.1.4\ or C:\Users\MYUSER\AppData\Local\Arduino15\packages\arduino\hardware\mbed_nano\2.1.0\

. Copy this file under the Arduino SAMD directory, i.e: C:\Users\MYUSER\AppData\Local\Arduino15\packages\arduino\hardware\samd\1.8.9\

. Copy this file under the Arduino Adafruit SAMD directory, i.e: C:\Users\MYUSER\AppData\Local\Arduino15\packages\adafruit\hardware\samd\1.6.3\

. Copy this file under the Arduino ESP32 directory, i.e: C:\Users\MYUSER\AppData\Local\Arduino15\packages\esp32\hardware\esp32\1.0.4\

. Copy this file under the Arduino STM32 directory, i.e: C:\Users\MYUSER\AppData\Local\Arduino15\packages\esp32\hardware\stm32\1.9.0\

Arduino IDE
Data forwarder - classifying data (Arduino)
Arduino Nano 33 BLE Sense
Arduino Portenta H7 + Vision shield
Arduino Nicla Vision
Arduino Nicla Sense ME
Espressif ESP-EYE (ESP32)
here
Arduino library source
here
known bug
following patch
mbed platform.local.txt
SAMD21 platform.local.txt
SAMD51 (Adafruit) platform.local.txt
ESP32 platform.local.txt
STM32 platform.local.txt
Arduino Nano 33 BLE Sense
Arduino Portenta H7 + Vision shield
Arduino Nicla Vision
ESP32 boards
Syntiant hardware tutorial
Recognizing sounds from audio
Keyword spotting
Image classification
Building a continuous motion recognition system
FOMO: Object detection for constrained devices
Board Manager
Download Arduino library
Include .zip library
Choose folder
Select example
Sketch example
Selecting the row with timestamp '320' under 'Detailed result'.
Copying the raw features.
Copying the raw features
Link ESP32 boards resources
Add espressif ESP32 boards
ESP32 board settings