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
  • Deploy as a customizable library
  • Deploy as a pre-built firmware
  • Edge Impulse for Linux
  • Deploy using a Docker container
  • Deploy to your mobile phone/computer
  • Latest build
  • Model optimizations

Was this helpful?

Export as PDF
  1. Edge Impulse Studio

Deployment

PreviousPerformance calibrationNextEON Compiler

Last updated 4 months ago

Was this helpful?

After training and validating your model, you can now deploy it to any device. This makes the model run without an internet connection, minimizes latency, and runs with minimal power consumption.

The Deployment page consists of a variety of deployment options to choose from depending on your target device. Regardless of whether you are using a or not, you can deploy your impulse to any device. The C++ library and Edge Impulse SDK enable the model to run without an internet connection on the device, minimize latency, and with minimal power consumption.

  • Model version: Quantized (int8) vs unoptimized (float32) versions.

There are 6 main deployment options currently supported by Edge Impulse:

From the Deployment page, select the Search deployment options search box to select and configure a deployment option:

Deploy as a customizable library

These deployment options let you turn your impulse into a fully optimized source code that can be further customized and integrated with your application. The customizable library packages all of your signal processing blocks, configuration and machine learning blocks into a single package with all available source code. Edge Impulse supports the following libraries (depending on your dataset's sensor type):

Library

Description

C++ Library

Arduino Library

WebAssembly

Cube.MX CMSIS-PACK

DRP-AI Library

OpenMV Library

Ethos-U Library

Simplicity Studio Component

TensorRT Library

TIDL-RT Library

Tensai Flow Library

Deploy as a pre-built firmware

To deploy your model using ready-to-go binaries, select your target device and click "build". Flash the downloaded firmware to your device then run the following command:

edge-impulse-run-impulse

The impulse runner shows the results of your impulse running on your development board. This only applies to ready-to-go binaries built from the studio.

If your training and testing datasets include a sensor data type that is not supported by a deployment target, the search box will include these greyed out with a Not supported label:

Edge Impulse for Linux

If you are developing for Linux-based devices, you can use Edge Impulse for Linux for deployment. It contains tools that let you collect data from any microphone or camera, can be used with the Node.js, Python, Go and C++ SDKs to collect new data from any sensor, and can run impulses with full hardware acceleration - with easy integration points to write your own applications.

Deploy using a Docker container

Deploying Edge Impulse models as a Docker container allows for packaging signal processing, configurations, and learning blocks into a single container that exposes an HTTP inference server. This method is ideal for environments supporting containerized workloads, facilitating deployment on gateways or in the cloud with full hardware acceleration for most Linux targets. Users can initiate deployment by selecting the "Docker container" option within the Deployment section of their Edge Impulse project.

Deploy to your mobile phone/computer

You can run your impulse directly on your computer/mobile phone without the need of an additional app. To run on your computer, click Launch in browser. To run on your mobile phone, scan the QR code and click Switch to classification mode.

Latest build

Download the most recent build from your project's deployment page under Latest build:

Model optimizations

EON Compiler

The EON Compiler lets you run neural networks using less RAM, and saving flash resource, while retaining the same accuracy compared to LiteRT (previously Tensorflow Lite) for Microcontrollers.

You can also select whether to run the unquantized float32 or the quantized int8 models. To compare model accuracy, run model testing in your project by clicking Run model testing.

To have a peek at how your impulse would utilize compute resources of your target device, Edge Impulse also gives an estimate of latency, flash, and RAM to be consumed by your target device even before deploying your impulse locally. This can save you a lot of engineering time, and costs incurred by recurring iterations and experiments.

You can also select different options on the deployment page:

Compiler options: TFLite vs . The EON Compiler also comes with an extra option: EON Compiler (RAM optimized) to reduce the RAM even further when possible.

Deploy as a

Deploy as a - for fully supported development boards

Use for Linux targets

Deploy as a

Run directly on your

Create a (Enterprise feature)

A portable C++ library with no external dependencies, which can be compiled with any modern C++ compiler.

An Arduino library with examples that runs on most Arm-based Arduino development boards.

A WebAssembly package that can be run in browsers or other JavaScript environments.

A CMSIS-PACK library for integrating Edge Impulse models with STM32CubeMX for STM MCUs.

Generate machine learning models using DRP-AI TVM with the DRP-AI Translator for use on Renesas RZ/ products.

An OpenMV library for vision-based projects, enabling efficient deployment on OpenMV cameras.

A C++ library for running machine learning models on Arm Ethos-U NPUs, optimized for low-power applications.

A C/C++ library package with Simplicity Studio Component file (SLCC) for integration with Silicon Labs' tools.

A library optimized for running inference on the GPU of NVIDIA Jetson devices using TensorRT.

A deployment option for generating machine learning models to use with Texas Instruments Deep Learning Accelerator (TIDL) on TI processors.

A library using Tensai Flow for running inference in building custom applications.

For these deployment options, you can use a ready-to-go binary for your development board that bundles signal processing blocks, configuration and machine learning blocks into a single package. This option is available for .

For a deep dive into deploying your impulse to Linux targets using Edge Impulse for Linux, you can visit the .

See how to .

When building your impulse for deployment, Edge Impulse gives you the option of adding another layer of optimization to your impulse using the .

Depending on your neural network architecture, we can also provide one extra layer of optimization with the .

EON Compiler
custom deployment block
fully supported development boards
Edge Impulse for Linux guides
run inference using a Docker container
EON compiler
model optimization
customizable library
pre-built firmware
Edge Impulse for Linux
Docker container
phone or computer
Learn more
Learn more
Learn more
Learn more
Learn more
Learn more
Learn more
Learn more
Learn more
Learn more
Learn more
fully supported development board
EON Compiler (RAM optimized)
Deployment options
Selected deployment option
Search in deployment options for the latest available options
Pre-built firmware for fully supported development boards.
Deployment option not supported for sensor data type.
Deploying using Edge Impulse for Linux SDKs
Docker container deployment
Deploying to your mobile phone or computer
Download the latest build.
Model optimizations
Resources estimation