Edge AI

Good day Folks,Firstly, is it possible to put NN weights on the external Flash, but not use it in memory mapped mode? BSP_XSPI_NOR_Init_t NOR_Init; NOR_Init.InterfaceMode = BSP_XSPI_NOR_OPI_MODE; NOR_Init.TransferRate = BSP_XSPI_NOR_DTR_TRANSFER; BSP_XSPI_NOR_Init(0, &NOR_Init); BSP_XSPI_NOR_EnableMemoryMappedMode(0);Secondly is it possible to run an AI model with NO flash access at all, i.e. to only use internal RAM for the case of a small model?I have an application that is already using the flash for storing data such as Logs & settings, via a XSPI Read/Write interface. However that interface is broken when configuring the flash to use the memory map.  The image classification STM32N6570-DK example code runs fine and can use the external flash, however it then appears I can't use the Flash for anything else but the NN tables & weights.I then tried updating the .mpool to exclude using the Flash, but the model is then not working or giving the same inference

Device : STWIN.Box (steval stwbxcs1a) with DATALOG2 3.1.0 firmware. WIFI : Updated the MXCHIP firmware   Objective: Running WiFi connection with APP and/or PnPL messages, Data retrieving with FTP.   Currently this is my application  as viewed in ST BLE Sensor app (iOS)   I have first tried with the iOS app, it doesn't let me to use the Wifi connect button or disconnect. Then,  I tried using PnPL messages with PythonSDK ( Board is connected by USB): message = PnPLCMDManager.create_set_property_cmd("wifi_config", "ssid", "Nothing")print(message)print(hsd_link.send_command(hsd_link_instance, device_id, message))message = PnPLCMDManager.create_command_cmd("wifi_config", "wifi_connect" , "password", "11223344")print(message)print(hsd_link.send_command(hsd_link_instance, device_id, message))message = PnPLCMDManager.create_set_property_cmd("wifi_config", "ip", "0.0.0.0")print(message)print(hsd_link.send_command(hsd_link_instance, device_id, message))time.slee

Hi,i'm currently working on a school project with the STM32n6570-DK and I'm pretty new into the STM scene. So my project will mainly be an object detection of different objects. The question is not directly about the STM, it's more about how to implement YOLO into STM. I heard something about the stm32-ai-model-zoo but I didn't go to deep into it yet. But is it possible to train a custom dataset with e.g. Google Colab and then implement it into STM or do I need a specific model-zoo file? And last but not least if someone has experience with STM32n6570-DK connected with YOLO, which YOLO version should I use? I heard YOLOv8n isn't that bad but I wanted to hear some more opinions. Hopefully someone can help me.Thanks for reading!Best Wishes

Hey!I'm trying to run my AI model on my NUCLEO-657X0-Q, following @Julian E. great guide in here.I have 2 questions:1. My model has some running issues during booting.In the FSBL code that suppose to jump to the application (JumpToApplication) there is some crash between the start of __set_MSP() and the start of _set_PRIMASK(), I would say it maybe an error in the pointer to the application code, but when I run it step by step in the IDE's debugger it works just fine.Do you have an idea of why would that happen / how to debug it? (I don't have printings at this stage, and turning in the LEDs in the middle of stack switch makes it fail in the debugger too).2. When my model do run (in the debugger) I get to the point where I start the epoch block, but no outputs / epoch end, and it just gets stuck in the loop. Does it happens because it's in debugger mode, or maybe my model is not compatible? when I validated it in CubeMX AI extension it looked ok.Is there any way to debug

Hello,Im trying to use the stedgeai generate command and want to move the data that gets located in the octoflash which seems to default to name=octoFlash offset=0x71000000 at times. Id like to relocate it at another position in the octoflash because i need to run several models. I tried adding the following that I found in an example and the output does not change at all from running it with the following options or not:--binary --address 0x810000or --binary --address 0x810000 --copy-weights-at 0xD0000000I found these examples via a search but as I said they dont seem to change the output.What am i doing wrong, I would like to change the default 0x71000000 to say something like 0x73000000, how can i accomplish this? I also tried to just change that in the network.c file, that didnt go well, just locked up the project at run time.  Thanks

Hello STMicroelectronics Community,I’m excited to engage with this platform and explore the wealth of resources available here. STMicroelectronics has long been at the forefront of innovation in semiconductors and embedded systems, and this community is a great space to exchange knowledge, ask questions, and collaborate with experts.From product forums and knowledge bases to learning materials and developer news, the community provides essential tools for both beginners and experienced professionals. I’m particularly interested in exploring:Latest product updates and development toolsBest practices shared in the knowledge base and FAQsLearning opportunities for embedded systems, IoT, and microcontrollersNetworking and collaboration with other developersLooking forward to contributing to discussions, sharing insights, and learning from the expertise within this community.Thank you for maintaining such a valuable resource for developers and tech enthusiasts!

Hi I am using the STM32N6570 with AI application models. I have seen the STM Model Zoo and STM32 AI Services. In the services, the application code is generated automatically based on the model selected from the Model Zoo. My question is: how do I develop my own AI application from scratch in STM32CubeIDE, without using the Python scripts? I have already run the Python scripts and they work fine, but I want to know how to build the application manually from scratch in CubeIDE.

Dear STEnvironmentItem Version / Details BoardSTM32N6570-DKOSUbuntu 24.04 LTSPythonConda, Python 3.10STM32AI Model Zoostm32ai-modelzoo-services/object_detectionOperation modedeploymentModelyolov8n_256_quant_pc_uf_od_coco-person-st.tfliteSTM32CubeIDE1.19.0ST Edge AI Core / STM32CubeAI10.2.0Local tool/opt/ST/STEdgeAI/2.2/Utilities/linux/stedgeaiDescriptionI am using the STM32AI Model Zoo deployment workflow on Linux to deploy a YOLOv8 object detection model to the STM32N6570-DK.The deployment pipeline reaches the final flashing stage and aborts because no ST-LINK device is connected, even though all previous steps (Neural-Art, code generation, project patching) completed successfully.Console log excerpt  building.. Debug flashing.. Debug STM32N6570-DK board programming is SKIPPED! -> No valid STM32 development board is connected (ST-LINK/swd port).. ... raise STMAICToolsError('Board programming failed!') E400: Board programming failed!   Expected behaviorEven when

Hello, I am working with the STM32N6570‑DK discovery kit and using the ST Edge AI Developer Cloud to generate code for my neural network. The tool produces a project with multiple files (model, runtime, headers, etc.), but I am struggling to understand how to actually integrate this into my STM32N6 application.Specifically, I would like to know:What is the expected input format (tensor dimensions, quantization type) and how do I feed my sensor/camera data into it?How do I retrieve the output from the inference (classification vector, bounding boxes, etc.) and use it in my application logic?How should I connect the generated code with my application (main loop, data acquisition from camera/audio, etc.)?I have looked through the STM32N6 examples and documentation, but they don’t clearly explain how to bridge the generated AI code with a working application. Any guidance or pointers to documentation/examples would be greatly appreciated.Thanks in advance! 

Hello,I am trying to test a deployed application for object detection.I have tested out of the box object detection application code.I can successfully build the project and I can run the code on my board.Unfortunately it doesn't detect any objects. It is supposed to detect person but it never detected.I have tried to use different pre-trained models and built and run them successfully but none of them work.The screen shows the live camera image, shows inference value and detected object number is always zero.Is there anything that I miss? By the way, the factory demo AI application works perfectly, it successfully detects people. So the hardware is OK.

Hi,I’m working on benchmarking and deploying a 33 MB half-INT8 model. During benchmarking, the measured inference time in ST dev cloud was about 264 ms. However, after compiling the model locally using STEdgeAI and deploying it, the inference time was found to be 405 ms.Additionally, when using the “Generate Code” option in the ST Dev Cloud, the generated artifacts—when deployed—showed the same inference time of 405 ms.I have verified that the weight and activation memory allocations are consistent with those used in the ST Dev Cloud (please refer to the attached logs).Could you please confirm whether this difference in inference time is expected, or if there is a known delta that should be addressed?logs:ST Dev cloud:>>> stedgeai analyze --model arcface_halfint8.tflite --st-neural-art custom@/tmp/stm32ai_service/7369b431-60e1-4924-9783-5f76cbd6b229/profile-27ac5fbe-1304-4c95-9d4e-44268ad9580f.json --target stm32n6 --optimize.export_hybrid True --name network --works

Hello,I am trying to run a Transformer model with Multi-head self attention on the STM32N6570-DK board.The model is this one : https://github.com/arx7ti/cold-nilmHere is what I did so far:Convert the .ckpt I got from the above repo into a .onnx format.Import and Quantize the model using your ST Edge AI Developer Cloud tool.Try to use stedgeai `generate` command:From the standalone tool on my Linux machineFrom STCubeMX plugginFrom the ST Edge AI Developer Cloud--> None of thoses attempts were succesful. Here is the command I used in the first case:stedgeai analyze --model cold_stm32_float32_final_ready_PerChannel_quant_random_1.onnx --st-neural-art default@user_neuralart_STM32N6570-DK.json --target stm32n6 --name network --workspace workspace --output outputI got several issues that I tried to fix in order to make the Developer Cloud tool quantize my initial onnx model:Removing Constant operationsConverting LayerNormalization layers in compatible layersRemoving Pow operatio

I’m currently testing my own quantized model on the STM32N6570-DK, using the official X-CUBE-AI application example.All Model Zoo examples (e.g. SSD, image classification) run perfectly with the same firmware and codebase.However, when I replace the model with my own quantized TensorFlow Lite model (Zero-DCE, converted with ST Edge AI Developer Cloud), the inference process freezes — even though the conversion and compilation complete without errors.I’m wondering if there is a hard restriction that prevents models from running when used RAM exceeds the on-chip SRAM (≈ 4.2 MB), even though the report shows that part of the activations are mapped to external HyperRAM.Here are the key parts of my Edge AI Core 2.2.0 generation report:ST Edge AI Core v2.2.0-20266Model name: zerodce_int8_192Total memory usage:----------------------------------------------Total: 11.643 MBWeights: 78.751 KBActivations: 11.566 MBMemory mapping:cpuRAM2 [0x34100000 - 0x34200000]: 1.000 MBnpuRAM3 [0x34200000 - 0x3

Hi all,I'm training a ST_yolox model to detect car. In my user_config.yaml i tried to use this : pretrained_weights: coco When i launch the training script i got this errorraise ValueError("\nUnknown or unsupported attribute. Received `{}`{}".format(attr, message))ValueError:Unknown or unsupported attribute. Received `pretrained_weights`Please check the 'training.model' section of your configuration file.But if i understand correctly we can use this variablehttps://github.com/STMicroelectronics/stm32ai-modelzoo-services/blob/main/object_detection/docs/README_TRAINING.md#2-7If someone can give me some explanation  

Hello community,I am currently working on deploying a full YOLOv8n (80-class COCO) model on the STM32N6570-DK and integrating it with the Object Detection example from the STM32 AI Model Zoo Service.I closely followed this tutorial:How to deploy YOLOv8/YOLOv5 Object Detection modelsand used the official quantization scripts here:YOLOv8 Quantization ScriptsModel & Quantization SetupBase model: yolov8n.pt (Ultralytics official)Exported to: SavedModel formatQuantization: done per STM32AI model zoo tutorialInput: uint8 (0–255)Output tested as:uint8 input / float outputuint8 input / int8 outputCase Input Type Output Type Result(1)uint8floatIncorrect inference — bounding boxes & class scores both wrong(2)uint8int8Bounding boxes coordinates always same (incorrect), classification correctAfter debugging case (2), I found:The bounding box coordinates remain constant across frames and detections, even before post-processing.The output tensors are quantized correctly, but the model’s raw

Hi all, According to the STM32N6 reference manual (see attached figure), both the diagram and the text description state that the ConvAcc should support 16-bit input × 16-bit weight (16×16) operations.To verify this, I designed a simple test model(see attached files): Input: 4×4 tensor (all ones)Kernel: 3×3 (all ones)Expected output: 2×2 tensor When I set the SIMD field to 1, the ConvAcc performs 8-bit input × 8-bit weight (8×8) operations, and the output results are correct as expected.When I set the SIMD field to 2, the ConvAcc performs 16-bit input × 8-bit weight (16×8) operations, and the results are also correct.My questions are:Does the ConvAcc really support 16×16 convolution (16-bit input × 16-bit weight) as described in the reference manual?If yes, how should I correctly configure the fields of LL_Convacc_InitTypeDef to enable 16×16 operation? (e.g., simd, inbytes_f, outbytes_o, kseten, etc.) 

Hi,Is there a way to enable weights compression while also using the STM32N6 NPU ?I saw there is Decompression unit in the reference manual where it tells the NPU compiler is the tool that generate codebooks offline (there is also mention of it in lib_aton.h too):This would help to reduce the model size in RAM. I've tried with models using a single channel in 1x1 Conv, but maybe this feature can only be used with some specific conditions ? Thanks!Alexis Murzeau  

Hi!It might be a *** beginners question but I'm quite lost...I'm trying to deploy my .ONNX model on my new NUCLEO-N657X0-Q board. The MCU on it STM32N657X0H3Q, seems to have 4Mb of RAM and no flash, but the board itself has 512Mb octaflash memory on it. My model is calculated as 2.71 MB of flash memory and 402 Kb of RAM. The ST edge developer tool claims that my board doesn't have enough resources and it won't create a suitable STM project for the IDE.My question is - is there a way to work with the board on AI models? is it a bug in the software or am I doing something wrong?Thank you!Amir

I am currently running the head_landmarks model from the official STM32 Model Zoo:https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/pose_estimation/head_landmarksThe ONNX model I used is face_landmarks_v1_192_int8_pc.onnx (downloaded directly from the github.The model can be successfully executed on the STM32N6570-DK board using the NPU, and the output results are correct.However, the inference speed is much slower than expected:Actual inference time on N6570-DK: ~3.6 seconds per frameReported time in the Model Zoo README: ~20.52 milliseconds per frameI would like to confirm:Is there any specific optimization or configuration (e.g., memory placement, quantization format, build options, or runtime parameters) required to achieve the published 20 ms performance?Could this large gap indicate that part of the model is running on the CPU instead of the NPU?Is there a way to check, from the generated ai_network_report or logs, which layers are accelerated by the NPU and which

I tried to use YOLOv8n instead of YOLOX in the people detection and tracking project. However, after modifying the related files (app_config.h, postprocess_conf.h, and main.c) and generating the model weights for YOLOv8n, the people detection and tracking demo does not work as expected. I can build and boot the program through dev mode, but no tracking boxes or text overlays (e.g. inference speed) are displayed—only raw camera capture is shown.What is the possible issue that happened, and how should I debug? What addtional infromation should i provide?

Hello,I’m currently exploring the STM32N6570 MCU for an embedded AI/computer vision application.I would like to know how to port OpenCV or other AI inference frameworks to this device.Could you please guide me on:Whether OpenCV is already supported on the STM32N6570 (or within the STM32N6 MPU/MCU ecosystem)?Recommended toolchain or SDK to build and deploy OpenCV (STM32MPU SDK / Cube.AI integration)?Any available examples or reference projects combining camera input + AI processing on this MCU?Thank you in advance for your help.Best regards,Bobban K. S.

I porting yolov11 model to stm32mp257. and The target detection model I use ,The output tensor is a combination of [1x7x8400] and [1x4x8400]. [1x7x8400] should be our classification because I have set 7 categories, and [1x4x8400] should be our positional information. 1. best.pt changed to best.onnxfrom ultralytics import YOLO# Load the YOLO11 modelmodel = YOLO("best.pt")# Export the model to ONNX formatmodel.export(format="onnx") # creates 'yolo11n.onnx' 2. best.onnx changed to best.nb./stedgeai generate -m path/to/onnx/model --target stm32mp25 3. into broad stm32mp257 ,and get output shape. #C++float *pfloatdata = static_cast<float*>(nn_model->get_output(0));std::vector<ObjDetect_Results> returnx = parseModelOutput(pfloatdata); std::vector<ObjDetect_Results> parseModelOutput(float* output, int num_classes = 7, float confidence_threshold = 0.55f){std::vector<ObjDetect_Results> detections;float *data = output;const int num_boxes = 8400

Hi,I'm trying to deploy a quantized ONNX neural network on a STM32N6-Nucleo and inference on board returns clipped values (see clipped-output.png). Previously, I've exported and quantized my torch neural network to onnx using the following configuration (the same is used in this end-to-end notebook example) torch.onnx.export( model, inputs, str(onnx_path), input_names=input_names, output_names=output_names, opset_version=15, export_params=True, do_constant_folding=True, ) quantize_static( str(onnx_path), str(int8_path), CalibReader(str(calib_path)), calibrate_method=CalibrationMethod.MinMax, quant_format=QuantFormat.QDQ, per_channel=True, weight_type=QuantType.QInt8, activation_type=QuantType.QInt8, reduce_range=True, extra_options={'WeightSymmetric': True, 'ActivationSymmetric': False})I've generated c-files related to this ONNX model using the following command : stedgeai generate --target stm32n6 --model "int8.onnx" \--st

Hey I am very new to this world. I study Data science and AI and I am currently writing my thesis on Federated Learning on Edge Devices.I am currently really really struggling on the device side. I have a quantized Yolov8n model fine tuned on weed detection dataset. My goal is to first upload the model onto my board which is a NUCLEO-U575ZI-Q with STM32U575ZIT6QU mcu. I tried the systemPerformance template from X-Cube-AI it works but I can not manage to run my code (mostly ChatGPTs). I tried a lot of different things but could only make the device print over UART for now. I need someone who is familiar with the app and mcu programming. Here is the code of my last attempt which failed. I tried to follow DigiKey's X-Cube-AI tutorial. The device seems to crash into the void HardFault_Handler(void).Here is my main.c :/* USER CODE BEGIN Header */ /** ****************************************************************************** * @file : main.c * @brief : Main program body ********

Hello,I'm following the instructions here: https://github.com/STMicroelectronics/stm32ai-modelzoo-services/blob/main/object_detection/docs/README_DEPLOYMENT_STM32N6.md after creating a virtual environment and installed all the packages from requirements.txt, but after running this command:python stm32ai_main.py --config-path ./src/config_file_examples/ --config-name deployment_n6_ssd_mobilenet_v2_fpnlite_config.yamlI am getting this error:STM32CubeIDE build failed: ""Error executing job with overrides: []Traceback (most recent call last):File "C:\Users\devan\Desktop\dev\STM32ModelZoo\stm32ai-modelzoo-services\object_detection\stm32ai_main.py", line 234, in <module>main()File "C:\Users\devan\Desktop\dev\STM32ModelZoo\stm32ai-modelzoo-services\st_zoo\lib\site-packages\hydra\main.py", line 94, in decorated_main_run_hydra(File "C:\Users\devan\Desktop\dev\STM32ModelZoo\stm32ai-modelzoo-services\st_zoo\lib\site-packages\hydra\_internal\utils.py", line 394, in _run_hydra_run_a