Edge AI

Which Keras version is Cube.AI using? I am having a problem with different versions. My model is being generated with Tensor Flow Keras 2.2.4-tf

Hello,i'm trying to validate the generated C model inside STM32CubeMX.I'm follow the Getting started documentation (UM2526 - rev 3)Like page 22, (4.5 Validating the generated C model) i wan't to use custom data-set (CSV file) on inputs and on outputs, but i have an error:I use previously self-generated values from STM32CubeMX validations files at "/home/user/.stm32cubemx/stm32ai_output/"Seem to be an issue with shape detection:With output csv file like this:# file created : 2019-11-13 15:28:48# output C-model features - 10/109.932567382812500000e+031.614522812500000000e+05-6.814434570312500000e+03-1.008901718750000000e+051.514196777343750000e+038.807486328125000000e+034.036426171875000000e+04-3.264179492187500000e+04-1.473285644531250000e+04-1.134370546875000000e+05I have the message: "Using reference output file, shape=(1, 10)"And if i use csv output file like this:# file created : 2019-11-13 15:28:48# output C-model features - 10/109.932567382812500000e+03,1.

Hello,I have some questions about using the code generatorI am currently using an NUCLEO-L476RG with X-NUCLEO-CCA02M1 and I want to use a tflite model for speech commands recognition.I use X-CUBE-AI version 4.1.0 with ApplicationTemplate.My AI initialization function generated by CubeMX:void MX_X_CUBE_AI_Init(void) { /* USER CODE BEGIN 0 */ /* Activation/working buffer is allocated as a static memory chunk * (bss section) */ AI_ALIGNED(4) static ai_u8 activations[AI_SPEECH_CMD_DATA_ACTIVATIONS_SIZE];   aiInit(activations); /* USER CODE END 0 */ }My function to retrieve the result of my prediction:int aiRun(const void *in_data, void *out_data) { ai_i32 nbatch; ai_error err;   /* Parameters checking */ if (!in_data || !out_data || !speech_cmd) return -1;   /* Initialize input/output buffer handlers */ ai_input[0].n_batches = 1; ai_input[0].data = AI_HANDLE_PTR(in_data); ai_output[0].n_batches = 1; ai_output[0].data = AI_HANDLE_PTR(out_data);   /* 2 -

The LSM6DSOX is one of the most innovative products in ST MEMS Sensors portfolio. And, of course, is onboard on the ST.box. Two pro-mode application directly shows part of the capability of this product:BLELowPower application: besides explaining how to use FreeRTOS and low power techniques to send the data to STBLESensor via Bluetooth, this application shows how to enable the capability of LSM6DSOX accelerometer to detect events like FreeFall, Single/Double Tap, WakeUp, Tilt and 6D orientation, also sending them to STBLESensor.BLEMLC application, on the other hand, directly shows how to program the LSM6DSOX machine learning core. In particular, this sample programs the accelerometer to run an activity recognition algorithm or a vibration monitoring algorithm and to send the results to application. To enable the activity recognition algorithm on the MLC, you have to select the STBLESensor [Activity Recognition] tab. You can also work in debug mode using the STBLESensor debug console an

The purpose of the FP-SNS-STBOX1, which is the main functional pack (FP) dedicated to the ST.box, is to provide examples that show how to build custom applications for the Pro Mode at a FW level. Its flexibility allows the ease portability of this FP across different STM32 microcontrollers, so that you can build your customized app not starting from scratch.The available pro-mode applications, a couple of which will be explained in the near future posts, are:BLEFOTA: uses ultra-low power implementation based on an RTOS for transmitting the data via BLE connectivity;BLELowPower: creates a BootLoader and an application for Firmware-Over-the-Air (FOTA) update;BLEMLC: programs the LSM6DSOX machine learning core for activity recognition or vibration monitoring and transmits the results via BLE;BLESensors: transmits sensor data in the simplest way for BLE trasmission;DataLog: saves the sensor data to the SD card (with FreeRTOS).Together with these applications, there are also a couple o

This is the log of Validation on DesktopStarting AI validation on desktop with random data...  Neural Network Tools for STM32 v1.1.0 (AI tools v4.1.0) -- Importing model -- Importing model - done (elapsed time 1.366s) -- Building X86 C-model . -- Building X86 C-model - done (elapsed time 35.235s) -- Setting inputs (and outputs) data Using random input, shape=(10, 270) -- Running X86 C-model TOOL ERROR: Weights are incompatible with model: 794136 loaded, 89864 found Creating report file C:\Users\Ilario Coppola\.stm32cubemx\stm32ai_output\network_validate_report.txt TOOL ERROR: Weights are incompatible&nb

Hy everyone,is possible to configure different values for MLC_ODR, FSM_ODR, XL_ODR and GYRO_ODR, or they must be set equals each others?Thanks and best regards,Leonardo M.

Hi,I am evaluating the MCL on LSM6DSOX. I am using a custom board, not the official motherboard, so Unico cannot communicate with my hardward to upload the configuration to the chip. I think I need to do the configuration manually (basing on Weka decision tree .txt file from manually generated .ARFF files) but I stucked...Anyone succeeded the configuration manually because I found no documentation on this? Could you please kindly give me some instructions on this? About how to change registers properly, which registers should be changed etc... I am reading the datasheet and application notes from ST but I am still much confused ...Thank you very much.

I'm wondering what the hard limit is for the MLC configuration in the LSM6DSOX. I'm using Unico 9.0.0.0 to configure four IIR2 filters and I loaded 4 decision trees (basically a small random forest). The Unico software created the .ucf file successfully. But when I wrote the configuration into the LSM6DSOX, it wouldn't give me any valid values in the register that held the decision tree outputs. The valid outputs were 1, 2, and 3, and the registers would only report 0.Is there a documented size limit on this MLC size? Even if there is a software bug in this version of Unico, it would be nice to be able to double-check to see that the .ucf file isn't configured to write past the end of the memory map.

In app note 5272 page 19, it shows that we can apply a user offset to the accelerometer (the USER OFFSET block). It also looks like the "Advanced functions" branches off before the Digital HP filter, Slope Filter, and User Offset. So my question is - if I apply a user offset, does the MLC use it? I'd imagine that the User Offset could be used as a type of accelerometer calibration, but I'm not sure if the MLC takes advantage of it.

In the Introduction to STM32Cube.AI part 2 there is link to colab notebook. Is this still active. I get an error. Is it possible to get acmes to the files.Thanks.

Hi experts!I am trying to use a NN made on Keras on my NUCLEO-F303K8 (MCU STM32F303K8T6).I've already configured my project, setup the USART2 and imported my NN (.h5)I successfully validated my project on desktop but when I tried to validate it on target, I got:ERROR: no connected board(s) or invalid firmware or the board should be re-startedI've updated all the firmwares and then tried to perform the validation with manual config (set USART2 with the right bound rate, SW4STM32, etc). And then now I am getting:No board connected to the machine with MCU STM32F303K8TXBut this is exactly what my MCU is.My versions:STM32CubeMX - 5.2.0Firmware - STM32Cube FW_F3 V1.10.0X-CUBE-AI - 4.0.0Eclipse - Neon.3 Release (4.6.3)System Workbench for STM32 - Ac6 - 2.5.0.201904120827I've also tried some solutions on other topics in the forum. None of them worked.

Hello,Please note that STM32CubeMX v5.0.1 is available on the web since yesterday.The main updates in this minor release are the following:some bug fixes Performance enhancements when loading ioc file and creating new projectSupport the Artificial Intelligence pack X-Cube-AIYou can either upgrade your current STM32CubeMX version to 5.0.1 or install 5.0.1 version directly from st.com as follows:[Upgrade] from STM32CubeMX 5.0.0 main window : [Fresh install] Download 5.0.1 from www.st.com/stm32cubemx:If you want to install the Artifical Intelligence pack through STM32CubeMX:If you face any issue with this new release, you can submit your question in the STM32 Q&A space using the relevant topics:STM32CubeMXMachine Learning & AI

I use STM32 Nucleo f429zi. I tried to replicate the application on the following link, and followed all the steps. I use USART3 with pins PD8 and PD9 (the manual says these pins are connected to ST-Link)https://www.youtube.com/watch?v=grgNXdkmzzQI use STM Cube IDE. On STM Cude IDE -> there is Debug option to flash the code on Board. However, when I try to Debug - there are errors generated in the app_x-cube-ai.c file.void MX_X_CUBE_AI_Process(void) { aiValidationProcess(); /* USER CODE BEGIN 1 */ int nb_run = 20; int res;   /* Example of definition of the buffers to store the tensor input/output */ /* type is dependent of the expected format */ AI_ALIGNED(4) static ai_i8 in_data[AI_IMAGECLASSIFICATION_IN_1_SIZE_BYTES];   AI_ALIGNED(4) static ai_i8 out_data[AI_IMAGECLASSIFICATION_OUT_1_SIZE_BYTES];   /* Retrieve format/type of the first input tensor - index 0 */ const ai_buffer_format fmt_ = AI_BUFFER_FORMAT(&ai_input[0]); // ai_input undeclared const

I am following this video this, i have successfully generated the code, validate it on desktop, build the code and flash using Truestudio it into STM32L476RB board. But when I try to validate it om my board it gives me error : board not found or invalid firmware or restart the boardI have also tried using different USART port, but no success. I have aslo flash the code using CubeProgrammer. but still same error on validating. CubeMX is able locate the port on which board is present but gives same error

Hi,I'm planning on implementing a motor fault detection through vibration signals on an embedded system and I found that ST has some good support for this, but what would be the ideal microcontroller/dev-board in order to implement this?On the AI page, there's only one board listed (B-L475E-IOT01A) but I see here in this forum that people are using very different ones? Any reasons why or why not?Thanks.

I'm using STM32L476G-DISCO , running the human activity keras model on Cube MX 5.2 and Using the AI package 4.0.0 , the validation on desktop is successful but I got a "read time error" everytime i run the validation on target also the validation on target runs successfully using the AI Package 3.4.0 ...Any help? or there still bugs using the package?this is the report of the validation on the attached file.

Hello,I have a question concerning the memory footprint of a neural network.Given a pre-trained neural network topology, how can I estimate the needed memory footprint.Are there any application notes that describes this in more detail.All feedback is welcome.Best regards,Joeri

I have a problem with STM32; when i connect my FC the STM32 it isn't detected from my pc, it doesn't appear in device manager. What is the problem? I have windows 10 a OS. I tried out to install cube programmer, zadig, but STM32 isn't detected. Thanks for the support!!

I'm trying to create the KWS model found onhttps://github.com/Shahnawax/KWS-ANN-KERASand I'm trying to implement it on an STM32F412G-DISCO MCU.I've followed the lines of https://www.st.com/content/ccc/resource/technical/document/user_manual/group1/69/bb/ec/5d/78/16/43/ce/DM00570145/files/DM00570145.pdf/jcr:content/translations/en.DM00570145.pdfby installing STM32CubeMX and then the X-Cube-AI component.When I load my Keras model as an h5 file and analyze it it works fine, but when I want to try to "validate on desktop" I've got the error seen on the screenshotError no "wish" detected. You have to install it manuallyI'd like to but I don't know where to find it. I'm running on Ubuntu 18.04 LTS.

Hello,I'm using STM32Cube.AI to implement a Keras model. It comprises three parallel branches and interaction takes only at the end place where I concat the branches.This is a simplified version to show the structure of the model:Unfortunately Concatenate is not supported :(. [AI:Generator] NOT IMPLEMENTED: Unsupported cOde generation for layer 'Concat' [AI:Generator] Python generation ended [AI:Generator] Invalid networkI think there is a demand to support the Concatenate function :) and it sholdn't be a big deal to integrate it in the code generator.The only solution I have at this moment is to save each branch separately and concatenate them in the user application.Is there another way to do this?Best regards,Oliver

I have two different input data (voltage, current) and output data in excel file. I need help to training the data using neural network method and how to program this in micro-controller and tested with real-time (machinery) input datas.RegardsHemavathi

..

..

..