Edge AI

 Dear all,For the STM32N6570-DK kit, is there a simple tutorial that shows the workflow for integrating a model generated by STM32Cube AI Studio into a project?A simple example would be: if an image is detected, we could turn on an LED on the kit.A guide would be very welcome. Many thanksCheersEmmanuel

Hello , i wanted to know what the process of debugging projects generated by Cube Ai Studio ? The automatic flash from cube ai studio to the board works , but when i wanted to debug the exported projected via stm cube ide , it doesnt work , can u please share a tutorial of whats the recommended process ?Im using the nucleo n657x0-q board  

Hi everyone,I’m working on a Driver Monitoring System (DMS) pipeline on the STM32MP25 and trying to understand both NPU utilization and overall performance limitations.Setup details:Platform: STM32MP25Framework: stai_mpu_network (using .nb models with use_hw_acceleration=True)Camera pipeline: GStreamer (appsink + cairooverlay)Current performance: ~15 FPSGPU load: ~25%Models in use:Face Detection (.nb) → runs every frameFace Landmark (.nb) → runs every 3 framesIris Landmark (.nb) → runs every 3 frames (same schedule as landmarks)YOLOv8n (Smoking/Calling, .nb) → runs every 3 frames (offset scheduling)Scheduling logic:Face detection: every frameLandmark + eye models: frame_count % 3 == 0YOLO model: frame_count % 3 == 1So not all models are executed every frame, but FPS is still limited to ~15.Questions:1. NPU verificationEven though .nb models are used with HW acceleration enabled, I don’t have confirmation that inference is actually running on the NPU.How can I verify that the NPU is bei

Hello ST Community,I am using the NanoEdge AI Studio to generate an anomaly detection library for an STM32L4 device (fixed installation, vibration monitoring). The library works fine, but I need to persist the knowledge learned during runtime so that after a power cycle or reboot, the device does not have to learn again from scratch.I followed the official documentation and community advice (including the solution marked as accepted in [this thread](https://community.st.com/t5/edge-ai/is-there-any-way-to-save-or-export-knowledge-which/td-p/123456)):- Added the `.neai` section in my linker script with `KEEP(*(.neai))`- Declared `extern char _sneai_knowledge[];` and `extern char _eneai_knowledge[];`- Computed `NEAI_KNOWLEDGE_SIZE = (uint32_t)_eneai_knowledge - (uint32_t)_sneai_knowledge;`- Implemented functions to save this RAM section to internal Flash and restore it on boot.However, `NEAI_KNOWLEDGE_SIZE` is **0** and the `.map` file shows that the `.neai` section is empty:```.neai 0x20

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Hello! Has anyone tried to deploy multiple neural networks on a stm32n6 at once? I'm developing an application which requires 5 NN. I created 5 .c libraries to deploy each NN on the NPU, I assume is because of the APIs that they overlap. So I'm curious if anyone has tried this and how it was solved. My last  option is to create a bigger models that contains all 5 on onnx, then deploy it, idk if someone knows if this could work to. 

Hello,I have recently installed and launched stm32cubeai studio latest version and it appears to be hung at launch screen with progress bar in motion.I have tried to close this from task window and tried uninstalling and installing it but still same issue.restarted the pc as well. What could be the issue. I have still 10 gb of hard disc space left. Br, 

Hello,I have downloaded the stedgeai for Linux on windows pc and transferred to Linux system running Ubuntu 22.04 .i want to know is it possible to install it in a headless mode where I don’t have to do or click anything on the gui as my Ubuntu system is all console based with no gui.whenever I try to run ./Linux installer I am getting this error ./-linux-onlineinstaller: error while loading shared libraries: libxkbcommon-x11.so.0: cannot open shared object file: No such file or directoryplease let me know how to install it on a headless system.bestR

Hi, I am trying to deploy a custom object detection model on the STM32N6-Nucleo board.I started with this:https://github.com/STMicroelectronics/stm32ai-modelzoo-services/tree/main/object_detectionThe training gets done, I get the performance metrics,python stm32ai_main.pyThe deployment code works fine, and I get the deployment complete message. But when I change from development mode to flash boot mode. It doesn't detect the camera.When I program the demo camera.hex filehttps://github.com/STMicroelectronics/x-cube-n6-camera-capture/tree/main/Binary/NUCLEO-N657X0-Qor object detection .hex file,https://github.com/STMicroelectronics/STM32N6-GettingStarted-ObjectDetection/tree/main/Binary/NUCLEO-N657X0-Q/USB-UVC-Displaythe camera works perfectly.Below are the training and deployment configuration files. These are the only files I changed. I would really appreciate any help in this regard. Thank you,# training configure.yaml file:general:project_name: custom_food_yoloxn_256logs_dir: logssav

Hello, I am trying to run Validate on target on a NUCLEO-N657X0-Q board using X-CUBE-AI 10.2.0, but it consistently fails. I would appreciate any guidance. Environment: Board: NUCLEO-N657X0-Q X-CUBE-AI: 10.2.0 STM32CubeMX / STM32CubeIDE: latest OS: Windows 11 Model: ONNX (MobileNetV4-Small based, input [1,4,224,224] float32, 2 outputs) What works: Analyze: OK (Flash 46.75MB, RAM 3.78MB) Generate: OK Validate on desktop: OK (acc=100%, nse=1.0) Board detected: ST-Link Virtual COM Port on COM5 Firmware flash reports: Start operation achieved successfully Problem: When running Validate on target via STM32CubeMX:     Running the ST.AI c-model (AI RUNNER)...(name=network, mode=TARGET)Proto-buffer driver v2.0 (msg v3.1) (Serial driver v1.0 - COM5:921600)Warning: C-network signature checking is skipped on ST Neural-ART NPULOAD ERROR: STM32 - read timeout 50000msValidation ended When running stedgeai CLI directly (after manual build via n6_loader.py):   &nbsp

Hi there,I am trying to deploy a quantized LiteRT (TF Lite) stateless LSTM model to a Nucelo-H723 using CubeMX, X-Cube-AI, and CubeIDE. However, when I go to compile the generated code in CubeIDE, I get the following linker errors (full console output at bottom of this post) related to undefined references to arm_vector_sum_s8, arm_nn_vec_mat_mul_result_acc_s8_s16, and arm_nn_activation_s16 from the NetworkRuntime1020_CM7_GCC.a static library. Any suggestions on what the fix might be?  Additional configuration information:We configure CubeAI inside of CubeMX to flash the system performance variant, and can load and analyze our LSTM model within the Cube-AI window in CubeMX with no issues.We also configure the H7 to use icache, dcache, CRC. We've been able to run other models (MLP) with no issues.Console Error:C:/ST/STM32CubeIDE_1.19.0/STM32CubeIDE/plugins/com.st.stm32cube.ide.mcu.externaltools.gnu-tools-for-stm32.14.3.rel1.win32_1.0.100.202602081

I have project consisting of FSBL+Application (writing AI code in application), I want to debug application code, but unable to do it . My core doubt is should I go via FSBL debugging or Application debugging while working with real time detection in AI ? I am very much confused in this application debugging, kindly address it.I have ensured the external memory loader in cube Programmer as well as in debugger settings is same & loaded .I had flashed the binaries in cube programmer FSBL-trusted bin at 0x70000000 and application trusted-bin at 0x70100000 and signed them also. I have attached a word document having screenshots of debugger settings and problems coming in debugging for each directly application and via FSBL. & one screenshot of external memory settings in .ioc file.Thanks & RegardsChanchal

Hi everyone,I am running into a persistent debugging issue with a generated STM32Cube AI Studio application on the NUCLEO-N657 board. The debugger seems to drop the connection when transitioning from the "FSBL" to the main application "Appli" , and subsequently locks up.My Environment:Board: NUCLEO-N657IDE: STM32CubeIDE v2.1.1Tool: STM32Cube AI Studio v1.1.0Note: The code is generated properly from STM32Cube AI Studio, and the model .hex file is downloaded successfully to the NUCLEO board: The Issue:During the first time debugging the program, it starts correctly.I run the "FSBL" application, and it executes properly.As soon as the execution jumps from "FSBL" to "Appli", the IDE throws a "Target is not responding" error.On any subsequent attempts to debug, it does not work at all and throws the error shown in the attached image. Console:" STMicroelectronics ST-LINK GDB server. Version 7.13.0Copyright (c) 2026, STMicroelectronics. All rights reserved. Starting server with the

In an Edge AI audio project that I'm working on, there is SAI2 peripheral that stops audio transmission after a while when the AI algorithm in the while loop of the main.c has done some inferences.The AI algorithm code was generated with Cube AI Studio and, when STM32CubeAI_Studio_AI_Process() is commented, the SAI peripheral works normally without interruptions.The board used is a NUCLEO-N657X0-Q.The version of software used is:-ST Edge AI Core: version 4.0.0-STM32CubeIDE: version 2.0.0-Cube AI Studio: version 1.1.0I've tried to search on internet for a solution and there is no material concerning it! I've tried to ask for help in other posts, but nobody helped me. Can somebody try to explain why and find a solution? I'm desperate and I don't know what to do for my project but withdraw. 

Using https://stm32ai-cs.st.com , starting with my .tflite model. My goal is to incorporate the new model into the STM32N6-GettingStarted-ObjectDetection example.  I am having some issues with versions.The ST Edge AI Developer Cloud produces a version of the model which differs from the version used by the support files in the example code. Case in Point - the function LL_ATON_LIB_Concat: the example has 6 parameters and code generated from the ST Edge AI requires 7 parameters.  Dr Google, suggests there is another option when I "Download C Code" from the AI Developer Cloud that I should also have an option to change the Target Output to "STM32CubeIDE Project", "System Performance Project", or "C-Project".I don't see that option. can you help.

 I am using STM32N6570-DK board . I am trying to build edge AI code in application mode with the help of generated STM32 cube AI studio generate project for IDE. I am following this article https://community.st.com/t5/stm32-mcus/how-to-build-an-ai-application-from-scratch-on-the-stm32n6570-dk/ta-p/825591 . @Julian E. This article includes AI using X-Cube AI , I just wanted to ask if I am using the project/ AI files generated by STM32 Cube AI studio, Will the configuration of external memory manager and addresses we are using in XSPI2 , RAMCFG , RCC settings all will remain exactly same ? I am asking this as I am getting some memory issues while debugging the application code.Also my code includes ADC+USART also, will this add to additional memory changes?

I need to do keyword spotting and for that I'm trying to classify using CSV files, but they end up being very large because there are so many files. Is there another type of file I can put in NanoEdge or another way to do this?

HardwareBoard: NUCLEO‑U5A5ZJ‑QMCU: STM32U5A5ST‑LINKSoftware / ToolsST Edge AI (via STM32CubeAI Studio)Target: STM32U5Model source: STM32 AI Model ZooModel used:miniresnetv2_s1_64x50_tl_int8.tflite(Audio Event Classification → ESC‑10)Steps FollowedImported the miniresnetv2_s1_64x50_tl_int8.tflite model into STM32CubeAI StudioSelected NUCLEO‑U5A5ZJ‑Q as the target boardKept default settings in all stepsRun on Target Generated code, built, flashed, and attempted (Step 4 – Validate) failed Observed IssueDuring Run on Target, validation fails with the following error:Run failed: Error: validate error: E200 (ValidationError): TARGET: Unable to bind the ST.AI runtime with "network" c-model E801 (HwIOError): Invalid firmware - COM8:115200I have attached the complete validation failure log for reference.Additional ObservationDuring Step 1 (Code Generation), I consistently receive the following warning from STM32CubeMX:Warning: Code GenerationMain Config: These peripherals still have some n

Hello!I have implemented one of the key parts of a speech recognition system on the STM32N6 without using the cloud. I would like to describe my results in more detail, but it seems to me that the “New Message” section is not the right place for that. Could you please advise me where it would be better to share this? 

Environment:Tool: STM32Cube.AI Studio (Windows Desktop)MCU: STM32N647 (Flashless)Board: Custom board (Non-ST DK/Nucleo)Issue: The Memory Pool option is locked to "Auto" and cannot be switched to "Manual" in the GUI. Since STM32N647 has no internal Flash, the default "Auto" mode triggers a Memory Overflow error as it attempts to place weights in non-existent Flash.Question: I have already prepared a custom mpool and Jason file. How can I explicitly point the Studio to use this JSON file via the GUI? I've checked the Wiki but couldn't find the specific input field...   

Hello, I'm trying to create a project with custom model via STM32 Cube AI studio, which uses STedge Core AI v. 3.0.0. Validation is performed on STM32N6570-DK. I'm getting following error:E200(ValidationError): TARGET: Unable to bind the ST.AI runtime with "network" c-model: [] E801(HwIOError): Invalid firmware - COM7:115200 When running same command from command line I see that progress goes to 74,8 % and then stucks until error is displayed.See attached failed report for details. 

I want to flash a custom object detection model on the STM32N6570-DK board. I followed these steps:1-I selected the model ST_YOLOXN_D033_W025_416_INT8_OBJECT_DETECTION_COCO_2017_PERSON.TFLITE from ST Edge AI Cloud, with the following parameters: input: 1×416×416×3 (8-bit), output: multiple outputs, model type: STAI_FORMAT_U8. I then generated the corresponding C files.2-I followed this path: ModelZoo service → application_code → object_detection → STM32N6 → Model → STM32N6570-DK, and replaced the original files with my custom model. The files I modified are:network.cnetwork_ecblobs.hnetwork_atonbuf.xSPI2.binstai_network.cstai_network.h3- In STM32CubeIDE, I configured the app.config.h file #define POSTPROCESS_TYPE POSTPROCESS_OD_ST_YOLOX_UI #define NN_HEIGHT (416) #define NN_WIDTH (416) #define NN_BPP (3) #define COLOR_BGR (0) #define COLOR_RGB (1) #define COLOR_MODE COLOR_RGB /* Classes */ #define NB_CLASSES (1) #define CLASSES_TABLE const char* classes_table[NB_CLASSES] =

For STM32N6570-DK , I was trying to generate STM32 IDE model regarding Edge AI, but it is not showing the option to download complete project . While this documnetation shows that we can download the complete IDE project . https://stedgeai-dc.st.com/documentation   

Hi all,I’m currently working on an Edge AI project using the NUCLEO-N657X0-q and I've run into a roadblock regarding multi-model deployment.I need to run 2 or 3 models on this MCU, scheduled via FreeRTOS. While I can get a single model working fine with STM32Cube.AI Studio, I’m struggling to find a detailed workflow for managing multiple instances.Specific questions:API Naming: How to properly configure Cube.AI to generate unique prefixes for multiple models to avoid link-time conflicts?Memory/Buffer Sharing: Is there a standard way to share a single activation buffer between models to save RAM, given they are called sequentially via FreeRTOS?Task Scheduling: Any known issues or best practices for calling stai_network_run() within a FreeRTOS task? (e.g. stack size or re-entrancy).I’ve checked the ST Edge AI Core docs but couldn't find a concrete example for this multi-model + RTOS use case. If anyone has a reference project or can point me to the right Application Note, I’d really