Edge AI

Hi,I am working on STM32N6570-DK Edge AI development on Windows and I am facing a board programming error when using stm32ai-modelzoo-services. I would like some guidance on the possible root cause and recommended troubleshooting steps.EnvironmentHost OS: Windows  11 Board: STM32N6570-DKTools and versions:STM32CubeProgrammer: v2.20.0 STEdgeAI: 2.2cubeIDE:1.19.0Python: 3.9 Model and datasetModel: st_yolo_xDataset: MS COCO Completed steps:Followed the official instructions to install and configure stm32ai-modelzoo-services on WindowsSuccessfully ran chain_tqeb (training / quantization / evaluation / benchmark) to completionThe quantization output folder contains:Quantized model artifacts (e.g. .tflite / .onnx as produced by the pipeline)A user_config file adapted from the official examplesProblem descriptionWhen running chain_qd or the deployment step for the STM32N6570-DK, the proces

Hello,I am encountering difficulties installing the ST Edge AI v3.0.0 application. During installation, the client cannot connect to MyST, even though my credentials work correctly on the ST website.I have also configured the HTTP proxy in the installer exactly as I have for other ST products, which functioned without issue. Nevertheless, the installer repeatedly displays the following error:“Issue while connecting to MyST. Please verify username, password, or internet access.”Could you please advise on how to resolve this problem?Thank you for your assistance.Best regards,JeM

Hello,At my workplace, we have purchased two STM32N6570-DK boards to implement our computer vision models. We are currently trying to implement the "st_yolo_x_nano 480x480x3" model from the ModelZoo. We followed these two links, strictly respecting the versions indicated for both STM32CubeIDE and STEdge: How to run AI models from Model Zoo on STM32N6  Introduction to Edge AI on STM32N6 (This second link uses STEdge 2.2, as per the reference).  The Issue: Every time I attempt to debug, the same thing happens: the program enters a HardFault_Handler(). This occurs due to an invalid/null pointer error returned by the function LL_ATON_Output_Buffers_Info() in buffers_info[0].scale.  The error happens specifically when it reaches line 36 of the code. According to the debugger, it cannot access memory at address 0x0.   Technical Details:Configuration: The app_Config.h file being used is the one provided in the ModelZoo.   /** *******************************

Hi everyone,I’ve been working on several Arduino and DIY electronics projects and was looking for a reliable way to get custom PCBs manufactured. I came across PCBWay, which offers affordable and fast PCB prototyping and manufacturing services.Some features I found useful:Easy PCB design upload and orderingQuick turnaround time for prototypesSupport for multi-layer and advanced PCB typesHelpful for Arduino shields, custom modules, and small electronics projectsI’m curious to know:Has anyone here used PCBWay for Arduino projects?Any tips for first-time PCB orders?Here’s the link for reference (non-commercial discussion): PCBWayLooking forward to hearing your experiences and tips for DIY PCB projects!

HiI have recently purchased the STEVAL-STWINBX1 (STWIN.box) board, and I’m exploring its use for predictive maintenance applications.I already know that FP-AI-PREDMNT2, together with DSH-PREDMNT, is available for the STEVAL-STWINKT1B kit.Could anyone let me know if a similar cloud application/dashboard (like DSH-PREDMNT) is officially available or supported for the STWINBX1 board?Any guidance or references would be much appreciated.Thanks.

Hello Everyone,   I am following below tutorial to create an ai application from scratch on my custom board.https://community.st.com/t5/stm32-mcus/how-to-build-an-ai-application-from-scratch-on-the-nucleo-n657x0/ta-p/828502I am able to run inference following the tutorial. Now, Instead of using UART for transmitting inference results, I want to use USB CDC to transmit the inference results. I have to enable USBX and ThreadX and move MX_X_CUBE_AI_Init() and MX_X_CUBE_AI_Process() from MAIN to app_ux_device_thread_entry. I have given below pool sizes:USBX_MEMORY_STACK_SIZE 1024*35TX_APP_MEM_POOL_SIZE 1024*8UX_APP_MEM_POOL_SIZE 46080When I run the application, I can see the device enumurates as USB serial device. Once I run MX_X_CUBE_AI_Process(), ux_device_class_cdc_acm_write() no longer works. If I comment out MX_X_CUBE_AI_Process(), I am able to continously write data to ux_device_class_cdc_acm_write and view in PC terminal. Further If dont invoke&nbs

Hi everyone,I am a junior engineer trying to learn the new STM32N6 (Nucleo-N657X0) board. My goal is to do some basic computer vision, and I am trying to interface it with a Raspberry Pi Camera Module V2 (Sony IMX219).To be honest, I am a bit overwhelmed by the complexity of the MIPI CSI-2 and DCMIPP settings, and I’m stuck. I’ve been trying to debug this for days but I can't get any image data.Here is what I have:Board: STM32N657X0H3Q NucleoCamera: Standard RPi Camera V2 (IMX219). It has its own crystal (X1) on the back, so I assume I don't need to generate a clock (MCO) from the STM32, but I'm not 100% sure if that's enough.I2C Works: I can read the Chip ID (0x0219) via I2C, so the connections seem okay.The Problem: I configured CubeMX with 2 Lanes, 950 Mbps PHY Bitrate, and RawBayer10 format. But when I run the code, HAL_DCMIPP_PIPE_GetState is always BUSY or failing.I am getting these random error codes in hdcmipp.ErrorCode every time I reset and try again. I don't know w

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Hello everyone — I’m running an AI image-classification model on the STM32N6570-DK and I’m stuck with a reproducible problem. The first inference completes successfully (EVENT_DONE), but on the second run the code gets stuck inside the runtime (run_epoch) with no event returned; on the third attempt it again stalls and then triggers a HardFault. I followed the ST tutorial here when creating the project:https://community.st.com/t5/stm32-mcus/how-to-build-an-ai-application-from-scratch-on-the-stm32n6570-dk/ta-p/825591Below are the full details, steps to reproduce, debug info, things I’ve tried, and the files I’ve attached.EnvironmentBoard: STM32N6570-DKModel: Image Classification (generated with STM32Cube.AI)Model and weights: placed in xSPI1 / xSPI2 (QSPI/XSPI) and also copied to internal SRAM for some buffersSRAM clocks: enabled for SRAM3, SRAM4, SRAM5, SRAM6Toolchain:  STM32Cube IDE  - Latest versionCubeMX / Cube.AI version: Latest VersionRTOS: (bare-metal / FreeRTOS) : Bare

Hello everyone,I’m testing the STM32N6 Neural-ART Hello World project (https://stedgeai-dc.st.com/assets/embedded-docs/stneuralart_stm32n6_projects.html#ref_hello_world_project). The project runs correctly when launched from CubeIDE, but fails when booting from Flash. I have observed that the boot from Flash does happen, I reach main() and If I do not call: SystemClock_Config_HSI_no_overdrive(); I can see my debug prints. However, if I do call it, nothing is printed. This strongly suggests a clock initialization issue or a difference between CubeIDE startup vs Flash boot. I also printed several clock values (CPU, SYS, XSPI1/2, etc.) and they differ between the two boot methods.Additional info:I modified the linker script:MEMORY { AXISRAM1_S (xrw) : ORIGIN = 0x34000000, LENGTH = 1024K }to:AXISRAM1_S (xrw) : ORIGIN = 0x34180400, LENGTH = 511KI also updated set_vector_table_addr() from misc_toolbox.c:SCB->VTOR = 0x34000000;to:SCB->VTOR = 0x34180400;Any insight w

Hello ST Community,I am encountering an issue with X-CUBE-AI v10.2.0 (embedded in STM32CubeMX 6.14.0) when trying to deploy a PyTorch model with a custom layer to an STM32H7.I have enabled "Custom layer support" in the X-CUBE-AI settings. I am required to provide a JSON configuration file to map my custom ONNX node to my C implementation. However, I am unable to pass the Analyze step due to a JSON schema validation error.Environment:STM32CubeMX: 6.14.0X-CUBE-AI: 10.2.0Target: STM32H7Backend: ST Edge AI Core v2.2.0-20266My Goal: I need to map the following custom operator to a C function prefix:Operator Name: SpectralConvDomain: MyCustomImplementation Prefix: spectral_convHeader: custom_spectral_conv.hThe Issue: I keep receiving the error E010(InvalidCustomLayerImplementation) from stedgeai.exe. It appears the JSON format I am using is incorrect for this specific version of the core.Attempts:1. Using the "prefix" key (Standard Key-Value format):JSON { "MyCustom::SpectralConv": {

Dear ST Team,I would like to ask whether you provide any tools or recommended workflow to measure the accuracy drop of a neural network when converting a .tflite model into the STM32 binary format using STM32Cube.AI.I am looking for a simple way to compare the model’s performance before and after conversion (e.g., running the same test dataset on both versions and checking accuracy).Do you have any built-in tools, scripts, or guidelines to benchmark this accuracy difference?Best regards,Adam

Hello,First of all, I'm a newbie in STM32N6 boards, so I apologize in advance if my question is fairly basic.I am trying to deploy an application for object detection using the STM32N6570-DK.I am following the "Object Detection STM32N6 Model Deployment" tutorial from stm32ai-modelzoo-services/object_detection/docs/README_DEPLOYMENT_STM32N6.md at main · STMicroelectronics/stm32ai-modelzoo-services · GitHub.In my case, for testing I am trying to deploy the "yolov8n_256_quant_pc_uf_od_coco-person-st_tflite" file as the object detection model in combination with the Python script "st32ai_main.py". I planned to use my own YOLOv8-based object detector with other classes, but first I tried with an available tflite yolov8 model from ST and Ultralytics.Running the script I get the following output:(st_zoo) PS C:\Users\andres\Desktop\stm32ai-modelzoo-services\object_detection> python stm32ai_main.py --config-path ./src/config_file_examples/ --config-name deployment_n6_yolov8_config.

I want to train some small DL models with Nvidia's RTX 50 series GPU, which can work properly only with CUDA 12.x. I've learned that the CUDA 12.x is only compatible with ONNX Runtime 1.17.x or newer version. So I want to know if the X-CUBE-AI 10.2.0 support ONNX Runtime 1.17.x or newer version? If it can, I would like use the X-CUBE-AI 10.2.0 to deploy my DL models on the STM32 MCUs.

Hi,I have build a custom model based on st yolox model.This model have an input of 480x480. Intput int8 and output float.Five classes for vehicles detectionQuantized and tested on stm32N6570-DK.Now i'm trying to add my model on this st examplehttps://github.com/STMicroelectronics/STM32N6-GettingStarted-ObjectDetection/tree/mainI'm following this tutorial to deploy my modelhttps://github.com/STMicroelectronics/STM32N6-GettingStarted-ObjectDetection/blob/main/README.md#application-build-and-run---dev-modeI'm using stm32cubdeIDE to program the cardAfter i programmed the network_data.hex, i'm trying to deploy and debug the application.But i have this error at this step : app_postprocess_initThe parameters Do you have en idea how i can resolve this ?

Hi,I am working on an STM32N6 project inspired by the peopleDetection example in which I try to run a yolov8 for instance segmentation.My pipeline uses camera (DCMIPP) + LTDC display + NPU inference (ATON / X-CUBE-AI).Each subsystem works correctly on its own:If I comment out the inference code in the main while, camera + LTDC work perfectly.If I comment out the camera/display code and keep only the inference (LED toggle + delay), the LED blinks, meaning NPU inference works correctly.This behavior is consistent when programming through CubeProgrammer.However, when everything is enabled at the same time, the display stays completely black.The portion of code that causes the issue is:LL_ATON_RT_Init_Network(&NN_Instance_Default);/* Run Inference */do{ll_aton_rt_ret = LL_ATON_RT_RunEpochBlock(&NN_Instance_Default);if (ll_aton_rt_ret == LL_ATON_RT_WFE){LL_ATON_OSAL_WFE();}} while (ll_aton_rt_ret != LL_ATON_RT_DONE);/* End of Inference */LL_ATON_RT_DeInit_Network(&NN_Instance_De

I’m currently testing the system with different configuration options, and I am trying to understand the impact of the --enable-epoch-controller option flag.Could you explain what functional or performance differences occur when this option is enabled versus when it is disabled?What I know so far is that when the option is enabled, the network_ecblob.h file is generated, and when it is disabled, the file is not generated.

Background:I am using the STM32N6570-DK board and trying to use the NPU for acceleration. For now, I want to run the official demo (x-cube-n6-ai-people-detection-tracking-main) to learn and verify the workflow. However, I have encountered some issues:1. When I debug and run the official x-cube-n6-ai-people-detection-tracking-main example directly in CubeIDE, the screen turns on but it only shows a gray display, as if the program is blocked or stuck somewhere.2. I have already programmed the network_data-dk.hex file to the board using STM32CubeProgrammer.I would like to know why the demo is not running correctly and what might be causing the program to get stuck.

Dear ST staff, Recently, while using STM32CubeMX with CubeAI to deploy the yolov8n_192_quant_pc_uf_od_coco-person.tflite model from object_detection onto the STM32N6570-DK development board.        Subsequently, following CubeAI's configuration, I programmed the network_atonbuf.xSPI2.bin file into the 0x71000000 memory locationIt is worth noting that this model is of type float, not int8 quantisation (as I understand it), and both member pointers inLL_Buffer_InfoTypeDef's const float *scale; /**< */ const int16_t *offset;are NULL. This should indicate that the output requires no normalisation conversion.void MX_X_CUBE_AI_Process(void) { /* USER CODE BEGIN 6 */ LL_ATON_RT_RetValues_t ll_aton_rt_ret = LL_ATON_RT_DONE; const LL_Buffer_InfoTypeDef * ibuffersInfos = NN_Interface_Default.input_buffers_info(); const LL_Buffer_InfoTypeDef * obuffersInfos = NN_Interface_Default.output_buffers_info(); buffer_in = (uint8_t *)LL_Buffer_addr_start(&ibuffersInfos[0]

Hey!I have a ResNet model that I have deployed on my NUCLEO-N657X0-Q, and I want to run a training step on the board itself. I'm fine even with training just the last 1-2 layers of my model on a single sample, so that the memory on the board will suffice. Is the NPU itself capable of it? is there a assisting tool for on-board training? Is it more likely to succeed by using the ARM cpu for that?Thank you,Amir

Dear ST Team, I'm trying to deploy models on STM32N6570-DK, and there is the issue:Followed tutorial below:stm32ai-modelzoo-services/object_detection/docs/README_DEPLOYMENT_STM32N6.md at main · STMicroelectronics/stm32ai-modelzoo-servicesEncountered error message below:(st_zoo) PS C:\Users\ricky\git\stm32ai-modelzoo-services\object_detection> python stm32ai_main.py --config-path ./src/config_file_examples/ --config-name deployment_n6_ssd_mobilenet_v2_fpnlite_config.yaml C:\Users\ricky\anaconda3\envs\st_zoo\lib\site-packages\mlflow\utils\requirements_utils.py:12: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. import pkg_resources 2025-11-30 21:33:53.440551: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudart64_110.dll'; dlerror: cuda

Hello,I have a solution which I am trying to port to STM32 MCUs which are able to run a machine learning/deep learning model. i wanted to know is it possible to generate a combined network.c from two models? Or do I have to generate multiple network.c files like network1.c, network2.c etc.How does it work? I have more than one(2-3) DCNN models.please adviceregards, 

Hello ST team,I am currently trying to deploy an rPPG (remote photoplethysmography) model to the STM32N6 using the ST Edge AI Developer Cloud.My model’s input tensor has the following 6-dimensional shape:(B, 8, 3, 36, 36, 3)B = batch size8 = temporal frames3 = feature channels36x36 = spatial resolution3 = RGB channelsI uploaded a fully quantized ONNX model (int8_qdq.onnx) through the web UI and ran Analyze using a custom profile.However, the analysis fails with the following error:  ST Edge AI Core v2.2.0-20266 2adc00962 WARNING: Unsupported keys in the current profile n6-allmems-O3 are ignored: memory_desc  > memory_desc is not a valid key anymore, use machine_desc instead INTERNAL ERROR: Unexpected combination of configuration and input shape To help reproduce the issue, I have attach