Frequently asked questions
Last updated
Last updated
The enterprise version of Edge Impulse offers in organizations. Try it out with our . To collaboration on your projects, go to Dashboard, find the Collaborators section, and click the '+' icon.
You can also create a public version of your Edge Impulse project. This makes your project available to the whole world - including your data, your impulse design, your models, and all intermediate information - and can easily be cloned by anyone in the community. To do so, go to Dashboard, and click Make this project public.
We use a wide variety of tools, depending on the machine learning model. For neural networks we typically use TensorFlow and Keras, for object detection models we use TensorFlow with Google's Object Detection API, and for 'classic' non-neural network machine learning algorithms we mainly use sklearn. For neural networks you can see (and modify) the Keras code by clicking ⋮, and selecting Switch to expert mode.
It depends on the hardware.
For general-purpose MCUs we typically use EON Compiler with TFLite Micro kernels (including hardware optimization, e.g. via CMSIS-NN, ESP-NN).
On Linux, if you run the Impulse on CPU, we use LiteRT (previously Tensorflow Lite).
For accelerators we use a wide variety of other runtimes, e.g. hardcoded network in silicon for Syntiant, custom SNN-based inference engine for Brainchip Akida, DRP-AI for Renesas RZV2L, etc...
By disabling EON we place the full neural network (architecture and weights) into ROM, and load it on demand. This increases memory usage, but you could just update this section of the ROM (or place the neural network in external flash, or on an SD card) to make it easier to update.
Yes. The enterprise version of Edge Impulse can integrate directly with your cloud service to access and transform data.
Simple answer: To get an indication of time per inference we show performance metrics in every DSP and ML block in the Studio. Multiply this by the active power consumption of your MCU to get an indication of power cost per inference.
More complicated answer: It depends. Normal techniques to conserve power still apply to ML, so try to do as little as possible (do you need to classify every second, or can you do it once a minute?), be smart about when to run inference (can there be an external trigger like a motion sensor before you run inference on a camera?), and collect data in a lower power mode (don't run at full speed when sampling low-resolution data, and see if your sensor can use an interrupt to wake your MCU - rather than polling).
Yes! A "supported board" simply means that there is an official or community-supported firmware that has been developed specifically for that board that helps you collect data and run impulses. Edge Impulse is designed to be extensible to computers, smartphones, and a nearly endless array of microcontroller build systems.
You can collect data and upload it to Edge Impulse in a variety of ways. For example:
If you use Edge Impulse in a scientific publication, we would appreciate citations to the following paper:
The minimum hardware requirements for the embedded device depends on the use case, anything from a Cortex-M0+ for vibration analysis to Cortex-M4F for audio, Cortex-M7 for image classification to Cortex-A for object detection in video, view our for more details.
Another big part of Edge Impulse are the processing blocks, as they clean up the data, and already extract important features from your data before passing it to a machine learning model. The source code for these processing blocks can be found on GitHub: (and you can build as well).
The compiles your neural networks to C++ source code, which then gets compiled into your application. This is great if you need the lowest RAM and ROM possible (EON typically uses 30-50% less memory than LiteRT (previously Tensorflow Lite) but you also lose some flexibility to update your neural networks in the field - as it is now part of your firmware.
Yes you can! Check out our documentation on into your Edge Impulse project, and using the !
Edge Impulse uses (a dimensionality reduction algorithm) to project high dimensionality input data into a 3 dimensional space. This even works for extremely high dimensionality data such as images.
Also see .
See on the Edge Impulse for Linux pages.
Using the Edge Impulse Studio data acquisition tools (like the or ), you can collect data samples manually with a pre-defined label. If you have a dataset that was collected outside of Edge Impulse, you can upload your dataset using the , , , or . You can then utilize the Edge Impulse Studio to split up your data into labeled chunks, crop your data samples, and more to create high quality machine learning datasets.
Transmitting data to the
Using the SDK
By (e.g. CBOR, JSON, CSV, WAV, JPG, PNG)
Your trained model can be deployed as part a . It requires some effort, but most build systems will work with our C++ library, as long as that build system has a C++ compiler and there is enough flash/RAM on your device to run the library (which includes the DSP block and model).
