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I am trying to debug AI application via FSBL (generated itself in STM32cube AI studio) , I have attached the debugger settings and Problem hereby. I had added some printf statements also in between , whose Teraterm output I have attached.Kindly help. RegardsChanchal 

 Hello everyone,I am encountering errors when running Semantic Segmentation, Object Detection, and Pose Estimation models on my STM32N6570-DK board using STM32 Model Zoo Services. Here are the details of my setup and the errors I am facing:Hardware and Versions:Board: STM32N6570-DKModels: Semantic Segmentation, Object Detection, Pose EstimationTools: STM32Cube.AI 4.0.0, GCC 14.3.1, HAL 1.3.0, STMCubeIDE 2.1.1I followed the README instructions from the STM32 Model Zoo Services repository to test the mentioned models. I compiled and flashed the firmware onto the STM32N6570-DK board.Errors Encountered: plaintextCopyBus interface interrupt BUSIF0 ERR: 0x0 BUSIF1 ERR: 0x20 Epoch Controller ERROR interrupt: EC_IRQ = 0x0000000a Epoch Controller opcode counter: 0x00000000 Epoch Controller label: 0x00000000 ATON_STD_IRQHandler()@871: irqs=0xlx assertion "0" failed: file "[...]/stm32ai-modelzoo-services/application_code/.../ll_aton_runtime.c", line 875, function: __LL_ATON_RT_IrqErr&nb

Hello!I am having trouble verifying NPU operation on the NUCLEO-N657X0-Q board.I am trying to test a simple INT8 FNN (Feed-Forward Neural Network) using an ONNX model. The project was generated via X-CUBE-AI within CubeMX. I have configured the AXI and Clock settings (CPU: 600MHz, NPU: 800MHz, NPU AXI: 800MHz) and, given the model's simplicity, I set it to use internal SRAM only.While the code execution seems to flow correctly without errors, I noticed that the output values are not being updated. The output buffer remains unchanged, or in some cases, still holds values that look like the input data. It seems the NPU's computation results are not being written back to the memory correctly.I would greatly appreciate any advice or suggestions, even if they aren't complete solutions!Environment Versions:CubeIDE: 2.1.1CubeMX: 6.16.1CubeProgrammer: 2.22.0ST Edge AI: 4.0ST-LINK: V3J17M10I have attached one of the project files I’ve been testing for your reference. Thank you always for your s

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

 Hello,I’m working on a project generated by STM32Cube AI Studio targeting the NUCLEO-N657X0-Q, using an XIP configuration with external XSPI flash ( It was said in another post that the generated projects from STM cube AI Studio follow XIP configuration).SetupBoard: STM32N657X0-QToolchain: STM32CubeIDE(2.1.1) + STM32CubeProgrammer(2.22.0)Project generated with STM32Cube AI StudioExecution mode: XIP (application in external flash)Runtime: Bare metal (LL_ATON_OSAL_BARE_METAL)Memory mappingModel location (from generated network.c):.addr_base = (unsigned char *)(0x71000000UL)Programming stepsI programmed manually using STM32CubeProgrammer:second_Appli.elf network_atonbuf.xSPI2.bin → placed at 0x71000000Debug configuration (FSBL project)I start debugging from the FSBL project.In Debug Configuration → Startup → Load Image and Symbols, I configured:second_Appli.elfDownload = OFF (already flashed manually)Load symbols = ONSo FSBL is loaded/debugged, while App and model are already p

Hi! I’m working with the STM32N6570-DK board using an object detection AI model, and I would like to manually modify the camera parameters. I believe settings such as exposure, white balance, and similar controls are currently configured automatically, but I want to use fixed/manual values instead.I already tried modifying the cmw_camera.c file, but nothing changed. Which files do I actually need to modify in order to set specific camera parameter values? Do I need to use a dedicated tool for this?I’m using the IMX335 camera module.  

Hello,I am currently experiencing difficulties using the STM32N6 NUCLEO board.My goal is to test NPU execution using X-CUBE-AI within CubeMX. However, I am stuck in an infinite loop inside the MX_X_CUBE_AI_Process function. Specifically, LL_ATON_RT_RunEpochBlock() continuously returns only the LL_ATON_RT_DONE status, preventing the loop from terminating as expected. do {ret = LL_ATON_RT_RunEpochBlock(&NN_Instance_Default);if (ret == LL_ATON_RT_WFE) LL_ATON_OSAL_WFE();} while (ret != LL_ATON_RT_DONE); I Can't get the LL_ATON_RT_DONE, only LL_ATON_RT_WFE.This issue persists even when using files generated via Cube AI Studio.I have configured the AXI and Clock settings in CubeMX (CPU: 600MHz, NPU: 800MHz, AXI: 800MHz). Since the model is simple, I am using internal memory only and have not enabled any security (TrustZone) or external memory settings.Environment Versions:CubeIDE: 2.1.1CubeMX: 6.16.1CubeAI Studio: 1.1.0CubeProgrammer: 2.22.0ST Edge AI: 4.0ST-LINK: V3

 Hi Team,I am currently working on a Driver Monitoring System (DMS) project using the STM32MP257F-DK board, and I have successfully integrated the OV5647 camera sensor.At this stage, I am facing an issue related to ISP calibration. The camera is currently running with auto exposure enabled, due to which the frame contrast is not constant for any detection. To stabilize and properly tune the image, a fixed YAML calibration file is required. However, a suitable YAML file for the OV5647 sensor is not available.From my understanding, the YAML calibration file needs to be generated using the ISP tuning/calibration tool. However, I tried following the complete process mentioned in the Wiki page  documentation, but the ISP tool is not detecting the board, which is blocking further progress.Could anyone please guide me on the following:How can I generate a custom YAML calibration file for the OV5647 sensor using the ISP tool?Is there any alternative or manual method to create the YAM

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

Hey there, I have run into an issue while trying to using the new edge AI-studio software. I try to make a run such that I am able to the reach the generate project page.However, currently, I am stack at the point where the pipe running in the EDGE AI studio tries to use the STM32 cube programmer, I think to run the binary and verify it works before proceeding to provide the generated project page.My set up includes a custom made board, set in DEV boot, and an st-link v3 pwr connected to the custom board. I can confirm that I am able to program the board using cube programmer. I am also able to debug the code programed to this board using XIP paradigm , there for I think the connection there is okay,The custom boards OCTO XSPI is very similar to that of the stm32n6570.DK as I am able to use the default bootloader for the dev board to be able to program it.How ever the logs from running the cube AI studio say Run failed: Error: STM32CubeProgrammer exited with code 1I attached

Hi,I am trying to install ST Edge AI Core 2.0.0 for an STM32N6 project. During the installation process, the installer asks me to specify the ST Neural-Art package archive.However, I cannot find the ST Neural-Art archive on this page:https://www.st.com/en/development-tools/stm32n6-ai.html#overviewI only found the ST Edge AI Core download, but not the separate Neural-Art archive required by the installer.Could you please let me know where I can download the correct ST Neural-Art archive compatible with ST Edge AI Core 2.0.0?Thank you.

Dear ST Community,I’m currently working with an ALIENTEK STM32MP257 development board and ran into an issue when deploying a model converted via the ST Edge AI online tool.The original model is a plant disease recognition model from the ST model zoo (in .tflite format). I used the ST Edge AI webpage to convert it to an .nb model for deployment on my STM32MP257 board. However, when I test the converted .nb model, the inference output is unexpected: it only predicts two classes — “Background_without_leaves” and “Corn__Northern_Leaf_Blight” — both with relatively high confidence. This happens regardless of the input image.In contrast, when I run inference directly using the original .tflite file on the same test images, the results are correct and show proper class diversity.I double-checked my test code and also consulted AI assistance, which suggested that the converted .nb model might be problematic.I’ve attached a compressed file conta

Hello,I am trying to create a project with Cube AI studio v1.2.My toolchain and libraries are well detected with Cube AI studio v1.1, and I can create a project.However, it is not the case for the Cube AI studio v1.2. The toolchain paths seem valid, but when creating a project no IDE is proposed at all, as shown in the attached image. Could you help me please ? No debug logs are generated. Versions :Cube AI studio : 1.2CubeMX : 6.17CubeIDE: 2.1.1CubeProgrammer : 2.22.0EdgeAICore : 4.0 

Hello ST Community, I am trying to run a TFLite model on the Neural-ART NPU of the NUCLEO-N657X0-Q board using STM32CubeIDE + STM32CubeMX and STM32Cube AI Studio. I followed the guide here: https://community.st.com/t5/stm32-mcus/how-to-build-an-ai-application-from-scratch-on-the-nucleo-n657x0/ta-p/828502 but with my own TFLite model. Model details: Architecture: 5x FullyConnected layers (41→32→32→16→8→1) with ReLU activations Input: int8[1,41], scale=0.0717, zero_point=-24 Output: int8[1,1], scale=0.0354, zero_point=1 Task: binary classification Problem: I have 31 test samples. Every sample produces the same output value 53 regardless of input. I verified the inputs are correctly quantized and different for each sample. What I investigated: Through per-epoch debugging I found the following epoch flow: EP0 (HW): reads input EP1 (HW): continuationEP2 (SW): DequantizeLinearEP3 (SW): Conv float → writes correct floats EP4 (hybrid): outputs ALL ZEROS EP5 (HW): reads zeros → always pr

Hi everyone,I'm encountering a blocking issue while trying to start an inference on a NUCLEO-N657X0-Q using STM32Cube AI  Studio and STM32CubeProgrammer.The connection to the board via SWD seems successful, but the process fails during the memory programming phase.        -------------------------------------------------------------------                       STM32CubeProgrammer v2.22.0                         -------------------------------------------------------------------  ST-LINK SN  : 002F00223234510333353533ST-LINK FW  : V3J17M10 Board       : NUCLEO-N657X0-Q Voltage     : 3.28V SWD freq    : 8000 KHzConnect mode: Under ResetReset mode  : Hardware reset Device ID   : 0x486 Revision ID : Rev Z Device na

Hello ST Community,I am currently developing a coin recognition vision model (MobileNetV4) to be deployed on the NUCLEO-N657X0-Q (STM32N657X0H3Q) board. I am trying to fully utilize the integrated NPU using STM32Cube.AI.[My Approach] To maximize feature extraction of the coin's engraved patterns, I am using a Photometric Stereo-like approach. I capture 4 grayscale images using a global shutter camera with 4 directional lights (Top, Bottom, Left, Right). My goal is to stack these 4 images into a single 4-channel tensor (H x W x 4) and feed it directly into the NPU. I have already modified the first Conv2d layer of my PyTorch model to accept in_chans=4 and trained it successfully.[The Issue] When trying to validate and deploy this model, I encountered the following constraints regarding the Neural Network Input:"Only RGB888 format is tested for Neural Network Input.""Only UINT8 format is supported."The UINT8 constraint is perfectly fine for our quantization pipeline. However, the RGB888

HIST EDGE AI can generate projects for various development boards based on the STM32U0 (e.g., NUCLEO-U083RC). However, the STM32U0 does not appear in the list of supported MCUs (only U3 and U5 are supported). Am I doing something wrong? BRGilberto

STM32N6 NPU – Why does ROM size increase for some models after Neural-ART compilation?Hello,I noticed an interesting behavior while comparing STM32N6 CPU execution vs NPU execution using ST Edge AI + Neural-ART.For a simple Dense/Fully Connected (ad.tflite) model, enabling the NPU did not significantly change the weights size:CPU target:weights (ro): ~270 KBNPU target:weights (ro): ~269 KBSo the compiled NPU representation stayed almost identical.However, for a VWW/MobileNet-like model using many DEPTHWISE_CONV_2D layers, I observed a large ROM increase after enabling the NPU:CPU target:weights (ro): ~42 KBNPU target:weights (ro): ~227 KBAt first I thought this was caused by the quantization format conversion:CPU:model_fmt : ss/sa per channelNPU:model_fmt : ss/sa per tensorBut I also tested ResNet model where the same quantization conversion occurs, without a major ROM increase.So it seems the quantization format change alone is not the main reason.My questionCan ST confirm whether:Neu

Hello,I am trying to deploy a custom image classification model (bacteria classification) on the NUCLEO-N657X0-Q board using the STM32 AI Model Zoo v4.0.0 deployment pipeline.What works:The pre-built flower classification hex from Binary/NUCLEO-N657X0-Q/USB-UVC-Display/NUCLEO-N657X0-Q_GettingStarted_ImageClassification-uvc.hex works perfectly — camera streams via USB UVC and classification is displayedTraining a custom EfficientNetV2-B0 model (128×128 input, 5 classes) completes successfully with 100% validation accuracyQuantization (ONNX PTQ uint8) completes successfullyThe Model Zoo deployment script (operation_mode: deployment) successfully:Compiles the model via ST Edge AI Developer CloudGenerates C code and librariesBuilds the STM32CubeIDE project (UVCL_ModelZoo configuration)Signs the binary using STM32_SigningTool_CLIWhat does NOT work:After flashing the generated files to the board, the firmware does not boot — no serial output (tested at 115200 and 921600 baud), no USB UVC cam

Hello,I am experiencing two issues that started after updating STM32CubeAI Studio. Both worked correctly before the update.---Environment- Board: NUCLEO-N657X0-Q- STM32CubeAI Studio: latest update (STEdgeAI Core v4.0.0)- STM32CubeIDE: 2.1.1- STM32CubeProgrammer: v2.22.0- ST-LINK FW: V3J17M10- Model: MobileNetV3-based custom model (ONNX), int8 quantized and float variants---Issue 1: Build failure – CMSE stub too far (float model)When using a float ONNX model (~19 MB weights), the Appli build fails with:ERROR: CMSE stub (.gnu.sgstubs section) too far (0x135e520) from destination (0x52f0)collect2.exe: error: ld returned 1 exit statusAdditionally, the linker reports multiple memory regions not declared in the generated `STM32N657X0HXQ_LRUN.ld`:warning: memory region 'AXISRAM2' not declaredwarning: memory region 'AXISRAM3' not declared... (AXISRAM4, AXISRAM5, AXISRAM6, XSPI1, XSPI2, ROM)This build succeeded before the update with the same model.---Issue 2: Flash failure – flash loader canno

 The following object detection models are not appearing on the STM32 Model ZooSSD_Mobilenet_V2(192x192)ST_YOLO_X_NANO(192x192)Have these models been removed or renamed?  

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. 

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  

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