Edge AI

Here i only found years old https://community.st.com/s/question/0D50X00009XkZkuSAFI have idea in application detect some cocncrete texts example "HEY ALEXA"and need good probability of detection in ADC received voice.Exist any new examples? Info?

Hello, I meet an error when I try to use the function ai_network_create() as the format of tutorial,The error report is:invalid conversion from 'ai_handle {aka void*}' to ‘const ai_buffer*{aka const ai_buffer_*}’However, I see every one use such function to initialize a instance for network, so how can I fix the problem?

HiI imported simple CNN to STM32L462RCT using STM32CUBE-AI v5.1.2 ApplicationTemplateI found that compressing the model has no effect on inference time.The aiRun procedure runs for 115ms both in 8-bit compression and "none" configurations although the accuracy drops a bit.I thought compressing float network parameters to uint8_t would not only save the memory but also speed up the inference.So, is compressing the model supposed to speed up the inference?

I want to use my converted model, and the first step as is mentioned in the instructions, is to use this function. But I don't exactly understand what should I pass as the first argument (network) to the function. should I pass "ai_linearmodel_data_weights_get()" ??

When importing a Keras LSTM model, the stateful=True parameter seems to be ignored by Cube.AI, resulting in a state reset after every prediction. Is it somehow possible to prevent this state reset in Cube.AI, so that the model can actually be used in an online setting?

I created a sklearn random forest decision tree, which I would like deploy to a STM32H743. I am unable to "Analyse" or "Validate" the model in X-Cube-AI Ver 5.1.2. All I get is an "internal error" message in the log file.The conversion to ONNX was done using https://github.com/onnx/sklearn-onnxCan anyone offer any help please?

Hello,I am currently using X-CUBE-AI library to run neural networks on a stm32h747. I successfully generated and validated on board single input and output networks, but when I execute the validation through CLI with a multi output network, I get an internal ERROR (text below).I can't find any information about this error on the web or in the documentation. Where could I find any information on the error ?$ stm32ai validate --mode stm32 -m Data/models_stm/343/model.tflite -d 7500000 --verbosity 2Neural Network Tools for STM32 v1.3.0 (AI tools v5.1.1)Using TensorFlow backend.-- Importing model-- Importing model - done (elapsed time 0.201s)-- Building X86 C-model-- Building X86 C-model - done (elapsed time 0.517s)-- Setting inputs (and outputs) dataUsing random inputs, shapes=[(10, 6400)] dtype=[float32]-- Running STM32 C-modelON-DEVICE STM32 execution ("network", None, 7500000)..<Stm32com id=0x7f0423450eb8 - CONNECTED(/dev/ttyACM0/7500000) devid=0x450/STM32H743/53/50xx and STM32H745/

 [webinar - Program decision trees in sensors with a Machine Learning Core]

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Hello,I am currently working with the acoustic scene classification (ASC) of the FP-AI-SENSING1 project.I tried to load the tflite model, which is provided at: STM32CubeFunctionPack_SENSING1_V4.0.2\Utilities\AI_Ressources\models\asc_keras_mod_93_int8.tfliteUnfortunately, I always end up with this error: ValueError: quantized_dimension must be in range [0, 1). Was 3.I always end up with this error no matter which tensorflow version I use.Can anybody help me with this issue?Thanks in advance,Ilkay

[webinar - Program decision trees in sensors with a Machine Learning Core]

[webinar - Program decision trees in sensors with a Machine Learning Core]

 [webinar - Program decision trees in sensors with a Machine Learning Core]

[webinar - Program decision trees in sensors with a Machine Learning Core]

[webinar - Program decision trees in sensors with a Machine Learning Core]

[webinar - Program decision trees in sensors with a Machine Learning Core]

[webinar - Program decision trees in sensors with a Machine Learning Core]

[webinar - Program decision trees in sensors with a Machine Learning Core]

[webinar - Program decision trees in sensors with a Machine Learning Core]

[webinar - Program decision trees in sensors with a Machine Learning Core]

[webinar - Program decision trees in sensors with a Machine Learning Core]

 [webinar - Program decision trees in sensors with a Machine Learning Core]

 [webinar - Program decision trees in sensors with a Machine Learning Core]

 [webinar - Program decision trees in sensors with a Machine Learning Core]

[webinar - Program decision trees in sensors with a Machine Learning Core]