Edge AI

When i try to login to the platform this page opens after a good wait:Error An error occurred while processing your request.Reference #221.dec41302.1782981612.27b98312https://errors.edgesuite.net/221.dec41302.1782981612.27b98312

Hello team,i thing there is a bug in ST AIoT Craft when loading multiple .csv dataset files.With figures, you can see respectively :2 .csv files 3 colums for accelero and 3 colums for gyro 1 for time_step and the last one for tag with 6 different valuesThe 1st and 2nd images show clearly the 2 files correctly tagged.The 3rd image show the Summary and if you zoom in section Labels, tou can note that only 1 file (and not 2) is concerned with tag.Import is done in image 4 with the 2 .csv files.And last image indicates that the last datalog is completely “unlabeled”. This dataset correspond to the missing “Labels Tag” file, that is “xxx10-40-10xx”So i concluded that the “tag” process for multiple .csv file is “not clear”.Error is already visible in image 3.Thanks for your feedback. 

I’m trying to convert an ONNX model which has a Conv layer that takes weights as input (has 2 inputs). Edge AI gives me:NOT IMPLEMENTED: Convolution with multiple not constant inputs not supportedAre there any short-term plans to support such layers in ST Edge AI?I also tried to change the name of the layer to some custom name (DynConv). The error in ST Edge AI changed to:NOT IMPLEMENTED: Unsupported layer types: DynConvI thought that I could bypass this error by using --custom with a JSON definition, but the JSON format is poorly documented. Also, many attempts at different formats seem to indicate that --custom only works for Keras. Is this correct?

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. 

Very disappointed and frustrated not to find the flat connecting cable.

Somesays ago, I successfully used STM32Cube.AI Studio to deploy a model to an STM32U585CIU6 board and test it.However, yesterday evening I upgraded STM32CubeIDE, STM32CubeProgrammer, and STM32CubeMX. After that, when I tried to run the same process again, I started getting an error.I searched online and found some similar cases. The suggested solution was to update all related software, so I updated the tools accordingly. However, the same error still occurs.I also tried using another laptop. On that laptop, I performed the same software upgrades and then tested with an STM32H743VIT6 board, but the same error occurred there as well.Because of this, I suspect that I may have missed updating some required software or package.Has anyone experienced this issue before or found a solution? I would greatly appreciate any advice or guidance.ps : stedgeai version is v4.0.1.   

  I'm looking at the test model and the optimized model in the Model Visualization view.However, the menu option to export as SVG is disabled—I can't select it.Do I need to change any other settings?

I am using seeed studio AI assistant board and I am not able generate the cube  project using StudioI am facing Memory Overflow Issue Is there any way to solve thisAnd Seeed Board is not supported when Board is selected during project creation 

Hi all,i have a small question about Validation of a trained model.Atm i only work on PC with NEAI Studio Version 5.1.1.I used only data out of a *.csv fileI trained (benchmark) the model with around 170 Lines and 16 axis to get an accuracy of ~97%.I then validated the library with some test data with same axis count and data preparation. The learning file has the minimum amount of lines as suggested from the benchmark, i tried more lines with same result.The validation shows that even good data is treated as abnomaly so i only get an accuracy of max. 50%.Are there any hints what i am doing wrong? Many thanks in advance for answers.BR’sMartin

Hi team, Reposting my earlier query as it appears to have been missed.How do I measure the peak RAM and peak Flash usage of an AI model?I need to calculate peak RAM and peak Flash from the IDE, but the model generated by ST Edge AI is in HEX format and is not included in the IDE's reported size. So, I'm adding the HEX file size to the IDE's peak RAM and peak Flash figures — is this approach correct? Details are shared below. KWS cpu+npuIDE  Flash = text + data = 205,404 + 1,616 = 207,020 bytes ≈ 202.17 KBRAM  = data + bss = 1,616 + 102,040 = 103,656 bytes ≈ 101.23 KBFor cpu+npu we flash the model in  hex format, so we need to add the peak ram and peak flash of hex file with IDE peak ram and peak flash. model size - Total Peak Ram and Peak FlashPeak RAM = (103,656 + 22,065 =  122.77 KB) Peak Flash = (207,020 + 22,065 =  223.72 KB)

Hello, I am trying to convert my Onnx model with the ST Edge AI Core tool to the .nb format for the STM32MP25-DK target.Since the results were not matching I stripped down the model into the single operations and tried to debug them It looks like the problem comes from the MatMul operation. My model looks like this: When I run my test script were I compare the Onnx model with the NB model, then I get the following results:================================================================================ ONNX OUTPUTS ================================================================================ [ONNX] matmul_result shape=(1, 16, 8) dtype=float32 min=-10.684526 max=7.463792 mean=-0.168686 values=[3.1317086219787598, -0.7650761604309082, 0.822991132736206, -6.243682384490967, -1.3690400123596191, 0.5091162323951721, -3.2105088233947754, 3.2010514736175537, 1.773209571838379, -2.338696002960205] ========================================================================

I try to use my own model which build form python and keras, however when I import the model to the tool it have error as follow:>>> C:\ST\STEdgeAI\4.0\Utilities\windows\stedgeai.exe validate --model C:\Users\user\Documents\battery_model_MLP.keras --batch-size 10 --mode host --name network --verbosity 1 --c-api st-ai --optimization balanced --target stm32g4 --memory-pool C:\Users\user\.stm32cubeaistudio\workspace\EISv3_SOCSOHpredictor\.ai\run\run-3\.ai\mempools.json --save-csv --workspace C:\Users\user\.stm32cubeaistudio\workspace\EISv3_SOCSOHpredictor\.ai\run\run-3\.ai\st_ai_ws --output C:\Users\user\.stm32cubeaistudio\workspace\EISv3_SOCSOHpredictor\.ai\run\run-3\.ai\st_ai_outputST Edge AI Core v4.0.1-20581 7ed50de05INTERNAL ERROR: File is not a zip fileRun failed: Error: validate error: INTERNAL ERROR: File is not a zip file. Please check the report for more details. Output: ST Edge AI Core v4.0.1-20581 7ed50de05PASS:   0%|          | 0/187 [00:00<?, ?it/s]              

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

hiI installed version 5.1.1 today and reactivated the license key, but I got the following error. nanoedge ai studio license has reached its activation limit Is there a solution?   I installed version 5.1.1 today and reactivated the license key, but I got the following error. Is there a solution?

Hi all,I came across an older thread here ("Anomaly Detection on ISPU", ISM330ISN) where the conclusion was that NanoEdge AI's learned knowledge cannot be preloaded onto the ISPU — the "Include knowledge from benchmark" option isn't available when targeting the ISPU, because the model has to relearn after every power cycle. One of you confirmed this is expected behavior given the ISPU sits directly on the sensor with no on-board non-volatile memory for it.I'm doing research on vibration-based bearing fault detection (a manuscript is currently under review at Mechanical Systems and Signal Processing, with validation across four public datasets — CWRU, Huang–Baddour, XJTU-SY, Paderborn), and this relearning constraint is directly relevant to what I'm working on: detectors whose healthy-state reference is small enough to be committed to flash and restored instantly, with no relearning window after a restart.Two questions for the community / ST team:1. Does this same constraint apply to th

 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

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

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

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.

 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 currently working with the NUCLEO-N657X0-Q board and the STM32N6570-DK board. I am deploying and testing my machine learning models using STM32Cube AI Studio.I have noticed something unusual. The model runs successfully on all target boards except the NUCLEO-N657X0-Q when the NPU is enabled. I am not sure why this is happening and would appreciate any insights or suggestions from the community.Another issue is that the NUCLEO-N657X0-Q shows significantly higher inference/execution times compared to the other boards. Surprisingly, even a Cortex-M7-based board appears to perform better in some cases. This is unexpected, especially considering the capabilities of the Cortex-M55 and NPU.Has anyone experienced similar behavior with the NUCLEO-N657X0-Q? Are there any known configuration issues, performance settings, memory considerations, or NPU-related limitations that I should check?Any help or guidance would be greatly appreciated.Thank you!

Hello,The Neural-ART compiler (atonn) crashes with an internal assertion when I run `analyze` on a quantized 1D CNN. The crash happens on the pooling unit and I cannot get past it.EnvironmentST Edge AI Core v2.2.0-20266 (X-CUBE-AI 10.2.0), Windows, via STM32CubeMX Target: stm32n6 Also reproduced on ST Edge AI Developer Cloud with ST Edge AI Core v4.0.1-20581 -> same assertion, so this does not look version-specific.Model Keras model exported to TFLite, full int8 quantization (int8 input and output, per-channel), batch size fixed to 1.Architecture (1D CNN for signal classification):  Input [1, 3000, 1]  3x { Conv1D -> BatchNormalization -> ReLU -> Dropout -> MaxPooling1D(pool=2, stride=2) }     conv filters = 128 (k=30), 32 (k=20), 4 (k=10), padding 'same'  Flatten -> Dense(512) -> BN -> Dense(128) -> Dense(32) -> Dense(4, softmax)Input tensor quantization has a non-zero zero-point (scale ~0.0366, zp -2).When converted, TFLite lowers the model to CONV_2D (x

I am unable to generate project [ consisting of .onnx format - model is using logistic regression] with the help of STM32 cube AI studio . I have attached the error in screenshot below. Can we not implement simple models like logistic regression while doing real time deployment in STM32N6570-DK? Kindly help.RegardsChanchal  

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