Edge AI

Hello,The latest version of X-CUBE-AI is 10.2.0, which comes with ST Edge AI Core v2.2.0.The latest Edge AI Core version is v3.0.0.The STM32 model zoo release 4.0 adds support for ST Edge AI Core v3.0.0.The STM32 model zoo release 3.2 adds support for ST Edge AI Core v2.2.0.Some of the models from the model zoo release 4.0 fail to generate code with the latest X-CUBE-AI with errors such as "Your model ir_version is higher than the checker's".What are the intended tools to build the models from the modelzoo 4.0 release for the STM32N6, or is only release 3.2 supported?Thanks!

Hello,I am experimenting with X-Cube-AI (v10.2.0) in STM32CubeMX(v6.16.1). I noted that the version of the ST Edge AI Core in this case is v2.2.0. Moreover, this makes that the Embedded Interface is the legacy one, instead of the the newest ST-AI. I wonder if there are plans to update X-Cube-AI so it uses the latest version of ST Edge AI (v3.0.0), and uses the latest the ST-AI interface.Thanks.

how to build object detection hex file in stm32cubeprogrammer using stm32 ai model zoo on stm32N6570-DK microcontroller, how to know the board is flash or not, how i see the live ouput in lcd on the board please explain clearly.

Hello,I have a custom yoloV8n model that works on STM32N6570-DK_GettingStarted_ObjectDetection. I have trained a yoloV11n model that i would like to test on the board.But on this exmple there is no yoloV11_ui.c ?How i can adapt this example ?Do i need to use app_postprocess_od_yolo_d_ui.c ?Regards,

I have been working with and familiarized myself with the GettingStarted applications, most specifically the object detection program found on GitHub: https://github.com/STMicroelectronics/STM32N6-GettingStarted-ObjectDetectionI would like to take the next step and load my own model onto the board, I like the setup the current code has to output the frames and show the inference processing on the LCD screen and would prefer to utilize the current sample code if possible.  My question is thisAm I able to load in my own model I trained using the ModelZoo services, replacing the current model after I convert it to C code. Or will I have to make my own code from scratch to do this Camera->Frame->NN->LCD pipelineAndDo I have to train the model using a certain resolution? I am cu

Hello,I'm trying to use 2 networks from the STM32 model zoo on the STM32N6. I would like to load the weights of both models into my external RAM, and the activations into the npuRAM3,4,5 and 6. I have selected the n6-extram profile for both models.I cannot figure out how to configure from X-CUBE-AI the address for the activations and weights, such that they don't overlap. The compiler reports:Compilation details ------------------------------------------------------------------------------------ Compiler version: 1.1.1-14 Compiler arguments: -i st_yolo_x_nano_256_0.5_0.4_int8_OE_3_3_0.onnx --json-quant-file st_yolo_x_nano_256_0.5_0.4_int8_OE_3_3_0_Q.json -g image_network.c --load-mdesc stm32n6.mdesc --load-mpool stm32n6__extRam.mpool --save-mpool-file stm32n6__extRam.mpool --out-dir-prefix neural_art__image_network/ --optimization 3 --all-buffers-info --mvei --cache-maintenance --Oauto-sched --native-float --enable-virt

Hello, I just started in stm32 system, I want to ask for stm32N6 series can use other camera module? such as camera product from Arducam where the product is made for raspberrypi systemwhich uses CSI-2 interface, here product link : https://www.arducam.com/product/64mp-quad-camera-kit/Thankyou

Hi, I followed the guide at the following link:https://community.st.com/t5/stm32-mcus/how-to-build-an-ai-application-from-scratch-on-the-nucleo-n657x0/ta-p/828502and I've run an example gently posted here that is similar (the link was posted on the comments of the guide):https://github.com/YousefDessouki/STM32_N6_NuceloBoardwithNPU.gitWhen I run the model posted on github on my Nucleo it's all fine and it will print output results on PuTTY console.However, when I try to change the neural network with one of those used for speech enhancement posted at the following link:

Hey there Community,I have two Issues I would appreciate aid with.Case1I have successfully created a Nano Edge AI Bench mark and I chosen the best model to run on the N6 Neucloe dev board. I need benchmark for another ML scenario, I intend on having both the Models running on the neucleo at the same time.Problem: I have wounder is given the interfacing with the generated libraries from Nano Edge AI, there Is support to run more than one model, i.e How would you call the inference on given model if both ":libneai.a" files share the same "NanoEdgeAI.h" interface.QN: Is there a work around for this scenario??Case2I have attached image file for this one, During the validation process There seems to be an Error with the enumeration. please see attached file form more information. 

 STM32N6570-DK Custom AI Model Flashing Error  Background / Official Requirements According to the official documentation for flashing the AI model onto the DK, three key .HEX files are required: ai_fsbl.hex STM32N6570-DK_network_data.hex STM32N6570-DK_GettingStarted_ObjectDetection.hex  Steps I Have Taken to Integrate My Custom Model Model Conversion: I successfully imported my custom model(st_yolo_x_nano_480_1.0_0.25_3_st_int8.tflite-STM32N6570-DK-code) into the ST Edge AI Developer Cloud. After quantization, I obtained a folder containing the three necessary files for the network data: network.c, network_ecblobs.h, and network_atonbuf.xSPI2.raw.

Dear ST community, We've been evaluating STM32N6 NPU since early january. We aim to deploy yolov8n / yolov11n inside the NPU.So far we've been able to use modelzoo provided by ST. We weren't able to do the same with yolov8 or yolov11n we trained using ultralytics scripts. Here the procedure we applied : - Download repo modelzoo and modelzoo-services- Verify that stm32ai_main.py associated with deployment_n6_yolo11n_config.yaml does work (it does, we just modify it to target Nucleo board)- Download / train yolov11n model using ultralytics framework use same input dimensions; export to Tflite format- Use  ST script to quantize tflite mode (tflite_quant.py) - Import the quantized tflite model and modify the .yaml to use our trained model instead of the ST version.- Let the design flow execute After that, nothing happen on the board. I've opened STM32Cube to see

I want to know what computer vision is and what are its real world applications. 

Can Nano edge AI be used to log data from STM32N6570-DK, which is connected to three sensors? If Yes, Is there any documentation available ? 

Hello,I am currently testing out the model zoo examples, following https://community.st.com/t5/stm32-mcus/how-to-run-ai-models-from-model-zoo-on-stm32n6/ta-p/814254 and the READMES. Programming the .hex files to the STM32N6-DK works and produces a running firmware. However, when I compile the application using the provided makefile and flash it onto the MCU as described in the README, the screen just remains black. I tested this for object-detection, face-detection and pose-estimation.Best,Steve

Hi all,I’m trying to compile an ONNX model that has float32 input and output. When I compile the model using ST Edge AI, the generated output ONNX shows both input and output as INT8 instead of float32. I wanted to understand:- Is this behavior because the model is being compiled specifically as an ONNX model?- Or is there a specific reason STEdgeAI is forcing INT8 input/output during compilation (e.g., quantization settings, optimization constraints, or tool limitations)? Any clarification on how STEdgeAI handles mixed-precision models and I/O types would be really helpful.Thanks in advance!

Hey,I am trying to run a model on the n6 npu.Currently most layers are run on the npu as the should but there are some layers which are executed on the SW target, even though the documentation states that the layer should be HW supported (in this case its the HardSigmoid layer). Changing the activation to a Sigmoid layer will make it run on the HW side. I thought it could be the case since in the documentation it states that sigmoid supports X*Sigmoid(X) and the problem layer is at a skip connection but i have early occurrences of the exact same HardSigmoid Skip connection pattern which are executed on the HW. Its only 2 of these blocks that are executed on the SW. Is this a bug or intential due to some optimization being done by the compiler.I attached an image of the model graph and Cube AI Analyze output.Thanks in advance

I've been developing an object detection application from scratch by using STM32CubeMX and IDE. The evaulation board that I'm using is STM32N6570-DK.I actually succeeded in implementing the continuous displaying of camera input. And then I tried to import the AI related code from the object detection code in STM32 Model Zoo Services.But unfortunately I found that there is a problem while executing Run_Inference() function.I traced the execution step and then finally I found that there is something wrong in handling NPU interrupts.In the ATON_STD_IRQHandler() in ll_aton_runtime.c file, irqs variable has the value of 0x16000000 which leads to exit().To find out the expected value, I downloaded the object detection code in STM32 Model Zoo Services, and then found out the irqs variable should have the value of 0x10000000.I would like to know the meaning of each bit in the irqs variable, which seems to be ATON_INTCTRL_INTREG variable.STM32C

Hi all,I’m currently developing a project on the STM32N6 and am looking for advice on the most robust development workflow. Given the complexity of the N6, I’d like to leverage the automation of CubeMX, but I’ve encountered several hurdles. CubeMX frequently freezes and generates wrong code. Moreover, code generation seems to vary between versions, making it difficult to maintain projects over time or follow existing tutorials (such as https://community.st.com/t5/stm32-mcus/how-to-build-an-ai-application-from-scratch-on-the-nucleo-n657x0/ta-p/828502). Also tutorials recommend to change generated code to make the projects running, which goes against the purpose of automatic code generation.I’ve noticed the ST Edge AI Model Zoo examples appear to use a hybrid approach. It looks like some functions where taken from CubeMX,

Hi all,I attached my project (CMake using STM32Cube for VSCode) that generates this behavior. I am trying to make a neural network run on the NPU of the STM32N6. I was following this tutorial: https://community.st.com/t5/stm32-mcus/how-to-build-an-ai-application-from-scratch-on-the-nucleo-n657x0/ta-p/828502 , except that I use XIP instead of loading the program to the RAM and use a custom small NN from an .onnx file (also included in the attatched .zip). However, the neural network returns wrong values independent of the inputs and when resetting, the values change.My suspicion is that it has something to do with the cache. However, I am cleaning and invalidating the cache as recommended here: 

I have a small problem upgrading the STM32N6-GettingStarted-ObjectDetection example project to a newer Edge AI version: I am trying to upgrade the existing yolo person detection net to the latest Edge AI revision (3.0) before exploring custom network.I think I updated all necessary parts as described in the Edge AI version update FAQnew static library (the non- _PIC version)updated the NPU subdirectory, added missing file to the STMCubeIDE projectin main.cchanged the NN name from 'Default' to 'network' to match the generated codereplaced npu_cache_init with npu_cache_enable (at this point, the inference loop was stuck waiting for completion , so..: )implemented npu_cache_enable_clocks_and_reset with the original CACHEAXI enabling codeAt this point, the npu seemingly started but causes this error:Bus interface interrupt B

HiWe're looking at an application where it may be helpful for an STM32N6x7 MCU to encode and store video from an ST image sensor then later recover and decode the video for analysis by the Neural-ART Accelerator in the STM32N6x7 - encoding and storing the video in real time then later recovering the video from storage and analysing it using the Neural-ART Accelerator would avoid the need for the analysis to be done in real time and ease power management.Video decoding on the STM32N6 series was last discussed in the community in March 2024 and at that time it wasn't on the roadmap but I thought I'd ask to see if this nay have changed since then?We were wondering if there was any roadmap now for H264 decoding on MCUs or if perhaps a difference approach might be better? For example, storing full quality encoded video and separately storing video downsized to fit the Neural-ART Accelerator as

   Hello, I would like to ask about a question regarding “STM32N6570-DK for gesture recognition control applications.”I have downloaded the official example x-cube-n6-ai-hand-landmarks-main and reviewed the DataBrief of the development board. Since the board itself does not contain internal Flash, th

I've been trying to flash a .tflite model onto my NUCLEO-L433RC-P, using STM32CubeMX and Keil uVision5 initially, and later with STM32CubeIDE. But for some reason I keep getting issues either with "Analyze" or with generating the code. I am on Windows 11. My computer is in the chinese language, but my system local is English (US).​Initially, while using CubeMX/Keil, I was able to successfully analyze my model after downgrading to the 7.1.0 version of Cube-AI. Before that, with the newest version, I get "TOOL ERROR: 'utf-8' codec can't decode byte in position x in position y: invalid start byte". But after analyzing and successfully running the model on desktop, when it comes to generating the code to run on my device, it refuses to generate any .h files nor the middlewares folder. I looked online and couldn't find any solutions, and decided to download CubeIDE as I thought that this might fix the issues. But after downloading CubeI

I am trying to deploy encoder only transformer model on STM32 by converting pytorch model to onnx format first and then using STM toolchain (STM32 CubeAI). However, I am constantly getting this error when I analyzed the model:TOOL ERROR: cannot reshape array of size 0 into shape () I tried to debug my model but couldn't find any issue in it. Any guidance will be appreciated. Thank you.

Hey Guys,I am currently in the process of running an object detection model on the N6. To make it accelerated on the NPU I quantize it during Training (QAT) in pytorch. I made some adjustments to the exported QAT model in onnx to make it run on the n6 NPU (I compared the original onnx output before and after my transformations and they are the same).When i try to validate my Model on the Desktop (MCU) however i get completely different output for the c model:This is a part of the onnx model in netron