Edge AI

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:

 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

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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:

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 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 t

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

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, 

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:

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

 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 Observe

Hello ! I am trying to build Blinky example on STM32N6570Dk eval board . And this is how my memory.x file looks  like below */MEMORY{FLASH : ORIGIN = 0x24180400, LENGTH = 256KRAM : ORIGIN = 0x241C0400, LENGTH = 512K}When I run cargo run --release , I am getting the error . Could anyone please point what might be the problem here  Running `probe-rs run --chip STM32N657 target/thumbv8m.main-none-eabi/release/blinky`WARN probe_rs::probe::stlink: send_jtag_command 242 failed: SwdApWdataError WARN probe_rs::probe::stlink: send_jtag_command 242 failed: SwdApWdataError WARN probe_rs::probe::stlink: send_jtag_command 242 failed: SwdApWdataError WARN probe_rs::probe::stlink: send_jtag_command 242 failed: SwdApWdataError WARN probe_rs::probe::stlink: send_jtag_command 242 failed: SwdApWdataError WARN probe_rs::

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

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).

 As the subject states, when I try to run the AI model from the original demo on the STM32N6570-DK board, the screen turns black and the LEDs switch to red.A few days ago, I attempted to run my own AI model. Using STM32CubeProgrammer, I selected the external loader MX25UM1245G_STM32N6570-DK (I believe this may have been incorrect). Then, in the Erasing & Programming section, I uploaded my .bin file, set the start address to 0x70380000, and started the programming process.After that, I tried to debug the STM32N6570-DK Getting Started Object Detection project in STM32CubeIDE. However, I encountered an issue in main.c with the error:#error "PostProcessing type not supported"which persists.Later, I decided to restore the original demo. I erased the external memory using STM32CubeProgrammer,then down

Hello st community!Does anyone managed to get a csi-2 camera working with Nucelo n657 can provide his project as reference, im trying to explore the DCMIPP but I'm getting lost so if any code provided so i can follow along.Thanks!

Downloaded latest version of  STM32-ISP-IQTune from the ST website today and installed it on my macbook, when I tried to launch the desktop application I get this attached error: 

Yesterday I built a model from a 256x256x3 .tflite. The Cloud produced a zip file that included a  network_atonbuf.xSPI2.raw file. This file is about 27KB in size.  What triggered the change from the network_data files?The network.c knew about this .raw file and pointed at it 0x71000000.  That's all good. I just rename it to .bin and loadi it with CubeProgrammer.  But what I don't understand is the test for version: #if LL_ATON_VERSION_MAJOR != 1 || LL_ATON_VERSION_MINOR != 1 || LL_ATON_VERSION_MICRO != 3 || LL_ATON_VERSION_DEV != 262 # error "Possible mismatch in ll_aton library used" #endifWorking with the STM32N6-GettingStarted-ObjectDetection as my base this version test failed in Make.  I just commented it out. Am I going to have a problem? 

As shown on the website below, the Arc Fault Circuit Interrupter (AFCI) solution is introduced as an Edge AI use case.https://www.st.com/content/st_com/en/st-edge-ai-suite/case-studies/direct-current-arc-faults-detection.htmlFor hardware development reference, would it be possible to purchase the development board shown on the page?It would also be helpful if you could share the schematic and BOM of the PCB.