Edge AI

I would like to clarify the data constraints for Benchmark workflow in NanoEdge AI Studio:What is the maximum supported number of lines for each imported CSV dataset during Benchmark?Is there any official documented hard limit on the number of rows, or only practical recommendations?Some community references mention a rough threshold of ~100,000 lines, and files exceeding this size may cause memory overflow, import truncation or failed Benchmark launch. Could you confirm whether this threshold is formally defined?

Hello,I am trying to create a custom AI model and run inference on the NPU of an STM32MP255F.At the moment, my goal is only to verify the model creation workflow. Therefore, inference accuracy is not important yet.Environment Target device: STM32MP255FInference engine: NPUNumber of training images: 116 (mouse images)Steps1. Dataset creation with RoboflowAnnotated all images. Resized images to 256x256. Exported the dataset in YOLOv8 format.2. Training and export with UltralyticsTraining:yolo detect train data=data.yaml model=yolov8n.pt epochs=100 imgsz=256Export:yolo export model=runs/detect/train/weights/best.pt format=onnx imgsz=256 quantize=8 data=coco83. Conversion with ST Edge AI Developer Cloud Uploaded the exported model to ST Edge AI Developer Cloud. Selected the platform: "STM32 Microprocessors embedding Cortex-A loaded with X-LINUX-AI" Executed Optimize. However, the following error is displayed and no .nb file is generated:Error while generating optimized file. Generation

hi all, I am trying to flash + run a relocatable / runtime loadable model. The problem is that the inference results are wrong (they never change, no matter what the buffer contains when I start the inference). If I use the stai_model files from the AI Cube Studio, the model works correctly, but is not relocatable… Am aware of matching the address where I flash the network_rel.bin and generating the model. (← see URL) I am using these docs. (← see URL) I am enabling all AXISRAM3...6 I generate the model as follows:stedgeai.exe generate -m "path/to/model.tflite" --input-data-type uint8 --no-inputs-allocation --no-outputs-allocation --target stm32n6 --st-neural-art --reloc# final output something like this:+--------------------------+-------------------------------+------+------------+--------+| name (addr) | flags | foff | dst | size |+--------------------------+-------------------------------+------+------------+--------+| xSPI2 (0x20009332

Hello,I've got a problem with displaying an image classification AI on my board. So far I've been following this tutorial:https://wiki.st.com/stm32mcu/wiki/AI:How_to_use_Teachable_Machine_to_create_an_image_classification_application_on_STM32starting from step 3, as I already have a trained AI model as .tflite file.The execution with the .yaml file is working fine, it's flashing but the display is only showing artifacts.Hardware details:STM32H747I-DISCO: MB1248-H747I-D03- LCD Daughterboard: MB1166-A09- Panel Controller: NT35510B-CAMS-OMV:Camera module adapter board: MB1683-CAMS-A02- Camera module:  MB1379-2V8-A05- Image sensor: OV5640The display was a newer version so I changed the defines inapplication_code/image_classification/STM32H7/Application/STM32H747I-DISCO/Inc/CM7/stm32h747i_discovery_conf.hto this:#define USE_LCD_CTRL_OTM8009A             0U#define USE_LCD_CTRL_NT35510                1U#define USE_LCD_CTRL_ADV7533                0UThe nt35510.c and .h files already existed in

Hi,I am working on a project using STM32H743ZITx and trying to deploy an ONNX model using X-CUBE-AI.My .ld file was doing just fine until I add the X-CUBE-AI in the middleware. It will split the BSS range across DTCMRAM and AXI‑RAM. The startup file’s BSS‑zeroing logic accesses invalid non‑hardware‑backed addresses between the two RAM regions and results in a HardFault right after Reset_Handler.My configuration is:MCU: STM32H743ZITx STM32CubeMX: 6.18.0 STM32Cube FW_H7: 1.13.0 STM32CubeIDE for Visual Studio Code: 3.9.0 Enabled X‑CUBE‑AI 10.2.1Files before and after the configuration of X-CUBE-AI are attached.

Hi everyone,I'm deploying a model on the NUCLEO‑H563ZI using NanoEdge AI Studio (v5.2.0). My full training dataset is about 2 GB, and I cannot downsample or reduce features – I must use the complete dataset.My PC specs:- CPU: 12th Gen Intel i7‑12700 @ 2.10 GHz (16 cores total)- RAM: 32 GB (31.7 GB usable)- Storage: 1.82 TB HDD (mechanical drive, WDC WD20EZBX‑00AYRA0)- OS: Windows 64‑bit- Cores allocated to benchmark: 16 (all available)In a previous test with only 40 MB of data, the benchmark took 40 hours on this same machine. If time scaled linearly, 2 GB would take ~2000 hours – impossible for our schedule.Since I cannot change the dataset itself, I'm looking for practical ways to reduce benchmark time through external measures. Specifically, I would like to know:1. Storage bottleneck – My dataset resides on an HDD. Would moving the entire NEAI workspace and dataset to a fast NVMe SSD (or even a RAM disk) dramatically cut the time? Does NEAI repeatedly read from disk during benchmark

Hello ST team, I am trying to run an ONNX model on a NUCLEO-U545RE-Q board using STM32Cube AI Studio I first tried with a model of about 281.75 KiB weights, and the generated project failed at build/link stage with an undefined UART symbol. To check whether the issue was related to memory size, I also tried a smaller model: - Model: model_seqlen20.onnx- Input: f32(1x20x25)- Output: f32(1x1)- Parameters: 32,129- Weights: 128,516 B / 125.50 KiB- Activations: 2,256 B / 2.20 KiB- Total AI flash reported: 130,190 B- Target: stm32u5- Board: NUCLEO-U545RE-Q- Firmware package: STM32Cube_FW_U5_V1.9.0- ST Edge AI Core: v4.0.1-20581- Toolchain: STM32CubeIDE The model generation step completes successfully. The reported memory footprint is small enough for the board. However, during the Run flow, STM32Cube AI Studio the build fails with: undefined reference to `UartHandle` The errors are: Middlewares/ST/AI/Misc/Src/aiTestUtility.c:- _put_c_driver: undefined reference to `UartHandle`- ioRawWriteBuf

how can we go initial stm32N6?

Please suggest abot EDGE AI 

我在尝试用STM32Cube AI Studio运行一个STM32F407ZGT6项目时遇到了“启动失败运行”错误，我不确定问题的原因。一开始运行正常，但之后错误一直出现。使用“主机”模式时，它正常工作。2026-07-13 10：49：46 错误：以下路径中找不到4.0.0版本的可执行文件：C：/*ST*/*STEdgeAI*/4.0，C：/*ST*/*STEdgeAI*/*2026-07-13 10： 49：47 错误：以下路径中找不到4.0.0版本的可执行文件：C：/*ST*/*STEdgeAI*/4.0，C：/*ST*/*STEdgeAI*/*2026-07-13 11：14：59 错误：从公共IP获取国家时出现错误：获取失败2026-07-13 11：15：00 错误：以下路径中找不到4.0.0版本的可执行文件：C：/*ST*/*STEdgeAI*/4.0，C：/*ST*/*STEdgeAI*/*2026-07-13 11：15：01 错误：以下路径中找不到4.0.0版本的可执行文件：C：/*ST*/*STEdgeAI*/4.0，C：/*ST*/*STEdgeAI*/*

Hello, I am working on a visual inertial odometry system. It basically contains a camera, a vision processing unit and an IMU. For vision processing part I was thinking of the STM32N6. Can you elaborate on the performance of the ST Neural-ART processsor? Can you elaborate on the inference time? How many images per second can it process?

Trained Yolov8n on the coco text dataset, trying to import into STCube AI Studio and receive the following error message.Run failed: Error: analyze error: INTERNAL ERROR: 0 vs. 11. Please check the report for more details. Output: ST Edge AI Core v4.0.0-20500 359356bb0 

I am working on STM32N6570-DK. If I want to deploy random forest model on this MCU, what is the procedure. As most of the time I was using STM32 Cube AI studio to generate IDE project and work on it . But , AI studio doesn’t support tree based models. So, what is the standard procedure for deploying random forest or tree based models on this MCU for real time detection ?  Kindly guideThanks & RegardsChanchal 

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

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 all, I merged the VENC_JPEG_Encoding example with a DCMIPP app. Both parts work on their own, but the JPEG encoding breaks (error -9 = JPEGENC_HW_BUS_ERROR) as soon as I turn on the DCMIPP capture. The JPEG input/output buffer is completely unrelated to the DCMIPP buffer. It’s just that as soon as I start the DCMIPP in CONTINUOUS mode, the JPEG encoder breaks… Very strange.Did anyone else experience this problem? What could cause this? How would I fix this? Best regards, rphii

Hello everyone, I can run my model through the stedgeai / STM32Cube AI Studio and see the following: But when I now go into the resulting application and build it myself, I see these memory sections, which tell a totally different story: How can I fix this? I added the AXISRAMx sections (they are not provided per default) which results in them being used by 0%, so that also can’t be right. Best regards, rphii

HelloST Edge AI: Guide for STM32MPU 、 Section 3.2.1. Referring to “Generate NBG from TensorFlow™ Lite or ONNX,”I converted the .tflite file downloaded from ModelZoo to .nb, but I’m getting an error.$ /opt/ST/STEdgeAI/4.0/Utilities/linux/stedgeai generate -m ./yolov8n_256_quant_pc_uf_od_coco-person-st.tflite --target stm32mp25ST Edge AI Core v4.0.1-20581 7ed50de05E102(CliArgumentError): stm32mp25 not recognized as target. Supported targets areispu --device DEVICEmlcstm32f4stm32l4stm32l4pstm32g4stm32f3stm32wbstm32n6 [--st-neural-art]stm32f0stm32l0stm32g0stm32c0stm32wlstm32v8stm32u3 [--hsp]stm32h7stm32f7stm32u5stm32l5stm32h5stm32c5stm32mp157cIf you change the target name slightly, you'll get the following error: $ /opt/ST/STEdgeAI/4.0/Utilities/linux/stedgeai generate -m ./yolov8n_256_quant_pc_uf_od_coco-person-st.tflite --target stm32mp2ST Edge AI Core v4.0.1-20581 7ed50de05E102(CliArgumentError): stm32mp2 is not availableCould you tell me how to convert a .tflite file to

Hi ST Community, I am trying to deploy a YOLO-FastestV2 INT8 object detection model on the STM32N6570-DK. The model is successfully converted and runs on the board, but I am facing issues with the detection results.Model InformationModel: YOLO-FastestV2 (INT8) Number of classes: 6 Parameters: 235,120Model InputInput_0_out_0uint8 [1,1,320,320]Model OutputsQuantize_297_out_0int8 [1,12,20,20]Quantize_313_out_0int8 [1,3,20,20]Quantize_311_out_0int8 [1,6,20,20]Quantize_330_out_0int8 [1,12,10,10]Quantize_346_out_0int8 [1,3,10,10]Quantize_344_out_0int8 [1,6,10,10]Issues I am FacingThe bounding boxes are not displayed at the correct positions on the camera image. The detected bounding boxes appear to be shifted or incorrectly scaled. Even when the camera is not viewing any object (or the camera feed is unclear), random bounding boxes continue to be displayed. These false detection remain on the screen even though there should be no valid detection.Verification PerformedI also tested the quanti

hey i am struggling to flash by custom onnx model on stm32N6570-DK . I tired using the stm32 cube ai cloud website but it only gives me option to generate c code and not a project or elf file . So i shifted to stm32cube Ai studio but it keeps me throwing this error -  (*) 'input'/'output' buffers are allocated in the activations bufferRunning the ONNX model...Running the ST.AI c-model (AI RUNNER)...(name=network, mode=TARGET)E200(ValidationError): TARGET: Unable to bind the ST.AI runtime with "network" c-model: [] E801(HwIOError): Invalid firmware - COM3:115200 can anyone help me to get started with the same

Board: STM32N6570-DKIDE/Toolchain: Zephyr RTOS + west, Zephyr SDK 0.17.4Repository: stm32-hotspot/zephyr-stm32n6-ai-people-detectionOS: Windows 11I am trying to run the STM32N6 AI people detection application in flash boot mode using MCUboot RAM_LOAD as described in the README. The application runs correctly in debug/development mode (west debug), but does not boot at all when built and flashed for boot-from-flash mode.No output is seen on the UART console after the MCUboot banner — inference never starts.Steps to Reproduce:Following the README exactly:# 1. Build west build -b stm32n6570_dk --sysbuild app -DSB_CONFIG_MCUBOOT_MODE_RAM_LOAD=y -p # 2. Flash model weights (board in Development Mode: Boot 0: L and Boot 1: H) west flash-weights # 3. Flash MCUboot + app (board in Development Mode) west flash # 4. Move boot switches to Flash Boot mode (Boot 0: L and Boot 1: L) # 5. Power cycle ``` --- ### Observed Behaviour After power cycle in Flash Boot mode, the UART console is silent — n

 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?  

Board: NUCLEO-N657X0-Q (MB1940), STM32N657X0H (Cortex-M55 + Neural-ART NPU), Device ID 0x486 Rev Z.Toolchain: ST Edge AI Core v4.0.1-20581 (STM32Cube.AI 12.0.1-RC2), atonn 1.1.3-275, NetworkRuntime1201_CM55_GCC, arm-none-eabi-gcc 14.3.1 (STM32CubeCLT 1.21.0). SUMMARYA small int8 QDQ model (TCN: Conv2D x7 + Gemm x2, macc ~2.37M, int8 weights ~20 KB) generated with "stedgeai generate --target stm32n6 --st-neural-art" runs to completion on the NPU but produces wrong outputs: high-input forecasts collapse to baseline. Independent cross-check: onnxruntime on the same int8 ONNX gives 9.664 (ppm, log-domain output dequantized) on our golden window; the NPU returns ~0.26. Confirmed on hardware with ST's own "stedgeai validate --mode target" (regression head rmse 8.78, cos 0.589 vs the reference). LOCALIZED ON-TARGET TO A SINGLE OPPer-layer on-target validation (backbone stage outputs exposed as ONNX outputs):- input projection Conv: cos 0.999 (correct)- block 0 (dilated Conv + Slice + residual

Hi ST Community,I need help regarding a version mismatch issue while deploying my AI model on STM32N6570-DK.Current EnvironmentI am currently using:ST Edge AI Core: v2.2.0-20266 (2adc00962) STM32Cube.AI: 10.2.0-RC1ProblemWhen I convert/generate my model using the current tools, the generated model version becomes:Generated model version: 1.1.3dev14But my board/application runtime is using:Runtime version: 1.1.3dev8Because of this version mismatch, I am unable to run the model properly.My UnderstandingI think I currently have two possible options:Option 1Install an older ST Edge AI version / STM32CubeAI version that generates a model compatible with:1.1.3dev8Option 2Upgrade my runtime/environment on the board so that runtime version becomes:1.1.3dev14QuestionsI need guidance on the following:Which option is recommended by ST? Where can I find the correct ST Edge AI / STM32CubeAI version that generates models for runtime 1.1.3dev8? How can I upgrade the runtime on STM32N6570-DK from 1.1.