Multi-impulse
Once you successfully trained or imported a model, you can use Edge Impulse to download a C++ library that bundles both your signal processing and your machine learning model. Until recently, we could only run one impulse on MCUs.
Feature under development
Please note that this method is still under integration in the studio and has not yet been fully tested on all targets. This tutorial is for advanced users only. Thus, we will provide limited support on the forum until the integration is completed. If you are interested in using it for an enterprise project, please sign up for our FREE Enterprise Trial and our solution engineers can work with you on the integration.
In this tutorial, we will see how to run multiple impulses using the downloaded C++ libraries of two different projects.
We have put together a custom deployment block that will automate all the processes and provide a C++ library that can be compiled and run as a standalone.
In this page, we will explain the high level concepts of how to merge two impulses. Feel free to look at the code to gain a deeper understanding. Alternatively, when we first wrote this tutorial, we explained how to merge two impulses manually; we will kept this process in the Manual procedure section but due to recent changes in our C++ SDK, some files and functions may have been renamed.
Multi-impulse vs multi-model vs sensor fusion
Running multi-impulse refers to running two separate projects (different data, different DSP blocks and different models) on the same target. It will require modifying some files in the EI-generated SDKs.
Running multi-model refers to running two different models (same data, same DSP block but different tflite models) on the same target. See how to run a motion classifier model and an anomaly detection model on the same device in this tutorial.
Sensor fusion refers to the process of combining data from different types of sensors to give more information to the neural network. See how to use sensor fusion in this tutorial.
Also see this video (starting min 13):
Prerequisites
Make sure you have at least two impulses fully trained.
As an example, we will build an intrusion detection system. We will use a first model to detect glass-breaking sounds, if we detected this sound, we will then classify an image to see if there is a person or not in the image. In this tutorial, we will use the following public projects:
Multi-impulse deployment block
The deployment block can be found here. To add it to your organization, head to this page: Edge Impulse Studio -> Organizations -> Custom blocks -> Deployment blocks.
Please note that the script works with EON compiled projects only and anomaly detection blocks have not been tested.
Modifying the generated libraries and merging them into a single library
If you have a look at the generate.py
script, it streamline the process of generating a C++ library from multiple impulses through several steps:
Library Download and Extraction:
If the script detects that the necessary projects are not already present locally, it initiates the download of C++ libraries required for edge deployment. These libraries are fetched using API keys provided by the user.
Libraries are downloaded and extracted into a temporary directory. If the user specifies a custom temporary directory, it's used; otherwise, a temporary directory is created.
Customization of Files:
For each project's library, the script performs several modifications:
At the file name level:
It adds a project-specific suffix to certain patterns in compiled files within the
tflite-model
directory. This customization ensures that each project's files are unique.Renamed files are then copied to a target directory, mainly the first project's directory.
At the function name level:
It edits
model_variables.h
functions by adding the project-specific suffix to various patterns. This step ensures that model parameters are correctly associated with each project.
Merging the projects
model_variables.h
is merged into the first project's directory to consolidate model information.The script saves the intersection of lines between
trained_model_ops_define.h
files for different projects, ensuring consistency.
Copying Templates:
The script copies template files from a
templates
directory to the target directory. The template available includes files with code structures and placeholders for customization. It's adapted from the example-standalone-inferencing example available on Github.
Generating Custom Code:
The script retrieves impulse IDs from
model_variables.h
for each project. Impulses are a key part of edge machine learning models.Custom code is generated for each project, including functions to get signal data, define raw features, and run the classifier.
This custom code is inserted into the
main.cpp
file of each project at specific locations.
Archiving for Deployment:
Finally, the script archives the target directory, creating a zip file ready for deployment. This zip file contains all the customized files and code necessary for deploying machine learning models on edge devices.
When changing between projects and running generate.py
locally:
You may need to include the --force-build
option to ensure correctness of the combined library.
Compiling and running the multi-impulse library
Now to test the library generated:
Download and unzip your Edge Impulse C++ multi-impulse library into a directory
Copy a test sample's raw features into the
features[]
array in source/main.cppEnter
make -j
in this directory to compile the project. If you encounter any OOM memory error trymake -j4
(replace 4 with the number of cores available)Enter
./build/app
to run the applicationCompare the output predictions to the predictions of the test sample in the Edge Impulse Studio
Want to add your own business logic?
You can change the template you want to use in step 4 to use another compilation method, implement your custom sampling strategy and how to handle the inference results in step 5 (apply post-processing, send results somewhere else, trigger actions, etc.).
Manual procedure
Some files and function names have changed
The general concepts remain valid but due to recent changes in our C++ inferencing SDK, some files and function names may have changed.
Download the impulses from your projects
Head to your projects' deployment pages and download the C++ libraries:
Make sure to select the same model versions (EON-Compiled enabled/disabled and int8/float32) for your projects.
Extract the two archive in a directory (multi-impulse
for example).
Rename the tflite model files
Rename the tflite model files:
Go to the tflite-model
directory in your extracted archives and rename the following files by post-fixing them with the name of the project:
for EON compiled projects:
tflite_learn_[block-id]_compiled.cpp
/tflite_learn_[block-id]_compiled.h
.for non-EON-compiled projects:
tflite_learn_[block-id].cpp
/tflite_learn_[block-id].h
.
Original structure:
New structure after renaming the files:
Rename the variables in the tflite-model directory
Rename the variables (EON model functions, such as trained_model_input etc. or tflite model array names) by post-fixing them with the name of the project.
e.g: Change the trained_model_compiled_audio.h
from:
to:
Tip: Use an IDE to use the "Find and replace feature.
Here is a list of the files that need to be modified (the names may change if not compiled with the EON compiler) in folders for both projects:
tflite-model/tflite_learn_[block-id]_compiled.h
tflite-model/tflite_learn_[block-id]_compiled.cpp
Rename the variables and structs in model-parameters/model_variables.h
model-parameters/model_variables.h
Be careful here when using the "find and replace" from your IDE, NOT all variables looking like _model_
need to be replaced.
Example for the audio project:
Example for the image project:
Merge the files
Create a new directory (merged-impulse
for example). Copy the content of one project into this new directory (audio
for example). Copy the content of the tflite-model
directory from the other project (image
) inside the newly created merged-impulse/tflite-model
.
The structure of this new directory should look like the following:
Merge the variables and structs in model_variables.h
Copy the necessary variables and structs from previously updated image/model_metadata.h
file content to the merged-impulse/model_metadata.h
.
To do so, include both of these lines in the #include
section:
The section that should be copied is from const char* ei_classifier_inferencing_categories...
to the line before const ei_impulse_t ei_default_impulse = impulse_<ProjectID>_<version>
.
Make sure to leave only one const ei_impulse_t ei_default_impulse = impulse_233502_3;
this will define which of your impulse is the default one.
Subtract and merge the trained_model_ops_define.h or tflite_resolver.h
Make sure the macros EI_TFLITE_DISABLE_...
are a COMBINATION of the ones present in two deployments.
For EON-compiled projects:
E.g. if #define EI_TFLITE_DISABLE_SOFTMAX_IN_U8 1
is present in one deployment and absent in the other, it should be ABSENT in the combined trained_model_ops_define.h
.
For non-EON-Compiled projects:
E.g. if resolver.AddFullyConnected();
is present in one deployment and absent in the other, it should be PRESENT in the combined tflite-resolver.h
. Remember to change the length of the resolver array if necessary.
In this example, here are the lines to deleted:
Prepare the c++ application
Clone this repository: https://github.com/edgeimpulse/example-standalone-inferencing-multi-impulse
Copy the content of the merged-impulse
directory to example-standalone-inferencing-multi-impulse
(replace the files and directory sharing the same).
Rename the variables in source/main.cpp
Edit the source/main.cpp
file and replace the callback function names, the features buffers.
Note: The run_classifier takes the impulse pointer as a first argument
Copy the raw features from the studio Live Classification page.
Compile and run
Enter make -j
in this directory to compile the project Enter ./build/app
to run the application Compare the output predictions to the predictions of the test sample in the Edge Impulse Studio.
Enter rm -f build/app && make clean
to clean the project.
Congrats, you can now run multiple Impulse!!
Limitations
The custom ML accelerator deployments are unlikely to work (TDA4VM, DRPAI, MemoryX, Brainchip).
The custom tflite kernels (ESP NN, Silabs MVP, Arc MLI) should work - but may require some additional work. I.e: for ESP32 you may need to statically allocate arena for the image model.
In general, running multiple impulses on an MCU can be challenging due to limited processing power, memory, and other hardware constraints. Make sure to thoroughly evaluate the capabilities and limitations of your specific MCU and consider the resource requirements of the impulses before attempting to run them concurrently.
Troubleshooting
Segmentation fault
If you see the following segmentation fault, make sure to subtract and merge the trained_model_ops_define.h or tflite_resolver.h
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