Edge AI

Hi everyone,I am trying to train a large dataset in NanoEdge AI Studio, where each class is around 500 MB in size.Totally 5 class.However, I am facing limitations related to the data structure, memory usage, and training workflow in NanoEdge AI Studio. I would like guidance on:Recommended data structuring or segmentation methods for very large datasetsBest practices to handle or reduce large data sizes (windowing, feature extraction, downsampling, etc.)Any tool limitations or configuration settings to be aware ofPossible alternative approaches if direct training with such large data is not supportedIf anyone has experience training large datasets with NanoEdge AI Studio, your guidance or references would be greatly appreciated.Thank you in advance for your supp

When using stm32n6 and enabling both GFX and AI models, the runtime continuously reports errors:"Epoch controller error interrupt: EC_IRQ = 0x00000008 Epoch controller opcode counter: 0x00000000 Epoch controller tag: 0x00000000 ATON_STD_IRQHandler()@851: irqs=0xlx Assertion '0' failed: file 'E:/TouchGFXProjects/AR_Glasses/Middlewares/AI_Runtime/Npu/ll_aton/ll_aton_runtime.c', line 855, function: __LL_ATON_RT_IrqErr"What could be the possible cause of this? Is it related to the XSPI2 bus? both GFX and cubeAI utilize XSP2.The model used is the official demo program: st_yolo_x_nano_480_1.0_0.25_3_st_int8.tflite from STM32N6-GettingStarted-ObjectDetection-main.I sincerely hope that st can help me solve this problem. Thanks

Hi everyone,I am trying to deploy a custom ONNX model on the STM32N6 using X-CUBE-AI v10.2.0 (ST Edge AI Core v2.2.0), but the tool crashes immediately during the analysis phase.Setup:Target: STM32N6Tool: X-CUBE-AI 10.2.0Model: GTCRN (from this repository:https://github.com/Xiaobin-Rong/gtcrn)Problem: When I run the analyze command, "stedgeai" fails with an internal Python exception. It seems to be failing on a specific attribute lookup. And here i

I am currently working on deploying a quantized Int8 TFLite model (ECG classification) onto the NUCLEO-N657X0-Q board using X-CUBE-AI.The Issue: I have successfully flashed the FSBL, Application, and Model Weights . The system boots up, and the inference loop runs without errors. However, the model always predicts "Class 0" (Normal) , even when I feed it hardcoded input data that corresponds to "Class 1" (Abnormal).The Problem: I performed a deep-dive debugging session by pre-filling the tensor arena (SRAM) with a garbage pattern (0x55) before every inference to check if the NPU is actually writing data.My Findings:Input Data: The input buffer is correctly updated for each sample (verified via printf).Layer-by-Layer Failure:Ep2 (MaxPool) & Ep5 (Logit

Hi!I'm new with STM32CubeIDE and STM32N657X0-Q. I'm working with them because of my bachelor's thesis. My model size es 73KB but 432KB because of activations.The input of my ML is data generated with CMSIS-DSP but I have issues in LL_ATON_RT_RunEpochBlock() line and, I can't see the output.I've followed these guidelines:https://community.st.com/t5/edge-ai/ll-aton-set-user-input-buffer-output-buffer-just-returns-wrong/td-p/842354https://community.st.com/t5/stm32-mcus/how-to-build-an-ai-application-from-scratch-on-the-nucleo-n657x0/ta-p/828502

Dear ST, I am using Nano Edge AI Studio for Embedded ML teaching at university. Recently, I faced the problem of installing licenses on class computers reporting that “License has reached its activation limits”.I understand that Nano Edge Ai Studio is free but I cannot activate multiple computers in the classroom with my personal free license. Could you please explain how to solve the issue (activate 10 computers in the classroom).Regards,Žilvinas Nakutis

Hello ST Community,I am currently working on my Final Year Project (PFE) and would appreciate your guidance regarding hardware and system design choices.Project Context (PFE)Objective:Develop a portable Edge AI–based system to detect worker fatigue in an industrial maintenance environment.Planned Technical Stack:STM32MP1 (Cortex-A7 + Cortex-M4)OpenSTLinux / YoctoTensorFlow LitePhysiological sensors: EC

1. Problem DescriptionI am running an int8 quantized 1D CNN model on STM32N6570 (NPU / ATON runtime).The full pipeline is:Modbus sensor input → preprocessing → NPU inference → postprocessing (softmax)The issue is that the NPU output (net_out) is identical for every inference, even though:Sensor values are updated periodically via ModbusPreprocessing produces different x_quant values each timeNPU inference runs without errorOutput buffer is properly invalidated after inferenceExample output log (repeats every inference):

Hello, I would like to know if I can use FP-AI-Vision to design my own application, for example, to detect trees in a yard. Or is it designed only for the three applications: food recognition, person presence detection, and people counting?

Hi After the latest update, I can't download C Code from Edge Ai Developer Cloud, there are no boards in the list.   fv

Hello. I am currently attempting to execute a MatMul operation on an NPU. I have implemented a simple TFLite file with a MatMul operation, as shown in the image below.When converting the model using ST Edge AI, I observed that Matmul operator is mapped to the MCU instead of the NPU, and the operation is converted into a Convolution. Furthermore, checking the network.c file reveals that it is being converted into a convolution layer with an extremely large stride.

Hello,I recently deployed multiple individual PyTorch models onto my STM32H7A3ZI-Q. My workflow ended up being:1. Convert from .pt -> .onnx -> .tf -> .tflite (quantized)2. Build an X-CUBE-AI project via CubeMX3. Swap models in same project using ST Edge AI Core CLI4. Writing scripts to semi-automate this flowIt works, but setting this up involved quite a few manual steps. Is this the most effective manner to approach this? Curious how others handle thisRegards,Ernest

I’m currently trying with the STM32N6570-DK board using the ST Edge-AI v2.2.0I would like to know whether a lightweight Transformer model (for example, a small Vision Transformer or a minimal Transformer encoder) can be successfully converted and executed on the STM32N6. I checked that batch_matmul, transpose, etc. are supported on the link (https://stm32ai-cs.st.com/assets/embedded-docs/stneuralart_operator_support.html )Currently, after converting the lightweight ViT model to tflite or onnx, I am trying to convert it using stedgeai and stm32cubeide, but only the following error messages appear and are not being convertedCode, model file, and error text: https://github.com/minchoCoin/lig

HelloI'm considering running an LSTM(RNN-based model) model file, on the STN32N657 NPU.However, I understand that the NPU is CNN-based and therefore cannot run on it.Is there a way to run an LSTM model file on the NPU?I understand that theoretically, it's possible to convert an LSTM to a 1D CNN or TCN.Has anyone converted an LSTM model to a 1D CNN or TCN and tested it on the NPU?Is there an example file available?Thank you. 

Hello, I am working on implementing an object detection application for the STM32N6570-DK, starting from the reference projects:STMicroelectronics/x-cube-n6-ai-people-detection-trackingSTMicroelectronics/STM32N6-GettingStarted-ObjectDetection

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

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  

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 46080

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

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 we

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

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: MyCustomImplementa

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