Edge AI

Hi,I want to download an old version 3.0.1 of Nano Edge AI studio but I could not find it from the website. It directly download the new version 4.5.0. How can I reach the version 3.0.1?

I tried to validate my Keras model on ST Cloud, but there seems to be '' stedgeai analyze --model emotion_model.h5 --st-neural-art custom@/tmp/stm32ai_service/5afbcf71-db0d-4f0d-81a1-3cd5102933d8/profile-2fb440ba-ebb2-486b-b795-d7287b8d6307.json --target stm32n6 --optimize.export_hybrid True --name network --workspace workspace --output outputST Edge AI Core v2.1.0-20182 a437ed3c9WARNING: Unsupported keys in the current profile custom are ignored: memory_desc> memory_desc is not a valid key anymore, use machine_desc insteadE102(CliArgumentError): Cannot use 'keras_dump'-type input files when using Neural Art compiler'' The error.I hope to verify the STM32N6570DK development board in the cloud, but if I choose the MCU platform, it seems that I cannot choose this development board. If I choose NPU, it seems that there will be the above errors again.

When using the previous V4.6.0 version, it wouldn't allowed it to be used unless I upgrade NanoEdge AI Studio latest version 5.0.2. So I've obliged...Now, one of the projects that I have for anomaly detection, that has several files for Valid Signals and Abnormal signals, when selecting those views, the main window just blacks out and the console error is:11:59:44.992 › Set title bar overlay <--- Last few GCs ---> [61444:0x2e980055c000] 54631 ms: Mark-Compact 4047.8 (4074.6) -> 4046.4 (4079.6) MB, pooled: 0 MB, 1344.48 / 0.00 ms (average mu = 0.269, current mu = 0.034) allocation failure; scavenge might not succeed [61444:0x2e980055c000] 55993 ms: Mark-Compact 4050.9 (4079.6) -> 4048.7 (4081.6) MB, pooled: 0 MB, 1335.40 / 0.00 ms (average mu = 0.157, current mu = 0.020) allocation failure; scavenge might not succeed <--- JS stacktrace ---> [61444:0424/120036.866948:ERROR:v8_initializer.cc(808)] V8 javascript OOM (Reached heap limit). So it seems pretty clear

Hello there,I am currently working with the STEVAL-PROTEUS1 evaluation board.To supply power, I have connected the board to my computer via micro USB (alternatively, a battery could have been used, but it should not make a difference).For data acquisition through USB using NanoEdge Studio, I established the STLINK-V3MINIE connection as shown in the attached picture.Although NanoEdge Studio detects "STMicroelectronics" on COM7, I am unable to receive any data.Additionally, I have noticed that the STEVAL-PROTEUS1 board itself does not appear separately in the Windows Device Manager. Only the STLINK-V3MINIE appears, even though both the STEVAL-PROTEUS1 micro USB and the STLINK-V3MINIE USB are connected to the PC simultaneously.Is this behavior normal, or could it indicate an issue with the setup?For reference, I recently flashed the firmware file Proteus_HS_Datalog_FUOTA_reference.bin.Firmware updates via STLINK-V3MINIE complete successfully, but NanoEdge Studio still fails to acquire any

I used the cmd on Windows and entered the following command: "stedgeai.exe analyze --target stm32f4 --name network -m CNN_Attention_MLP1.onnx --verbosity 3 --no-onnx-optimizer". Then I got the error message as mentioned in the title. When the "--no-onnx-optimizer" parameter was not used, I would get similar DEBUG information. They were all "Error in execution of pass type(OPT_RM_TRANSPOSE_MATMUL) id(16)" as shown in the subject, which really confused me. Since I had turned off the ONNX optimizer, it was strange that it still reported an error here. The attachment is my debug log and the ONNX model.

As you see,i used cubemx create project which used cubeai.But i don't konw how to used it.What libraries are included in the code generated by CUBEAI? How should I structure my code and what is the underlying logic? What types of return values should I expect? Are there specific explanations, documentation, papers, or tutorials available for reference?

Hello, I am using X-CUBE-AI to run an AI model on an embedded system. While executing the following line of code: batch = ai_mnetwork_run(net_exec_ctx[0].handle, ai_input, ai_output); a Hard Fault error occurs. What’s puzzling is that the error initially occurred, but without any code modifications or corrective actions, the issue temporarily resolved itself. However, after some time, the same Hard Fault error reappeared. Has anyone encountered a similar issue, or could you suggest possible solutions?I would greatly appreciate your help. Thank you!

When i used cubeai,i met some problem which i click anaylze the error is  "Unsupported layer types: DynamicQuantizeLinear, ConvInteger".And i saw my model's quantizelinear manner is dynamic.so i want used pycharm to solving my error.i want make a static quantization and pruning to my trained YOLO model.my model's type is PT file,can you give me a code to solve my problem and i will very appreciate for your help!

Can I run a Machine Learning model using Tensor flow lite or Edge Impulse on STM32F103C8T6 with 64kb flash.Actually I don't have any choice but to try to run the ML model on Blue pill, because my there is unavailability of other MCU boards at the institute. Any kind of help would be appreciated !

After automatically searching for the best NanoEdge AI library, only the library ID and model name are displayed, and the specific content of the model, such as the neural network structure, cannot be found. I want to know about this aspect.Can you help me solve this problem? Thank you.

Under STM32CubeIDE I generated a neural network form my Nucleo board with X-CUBE-AI 9.0.0 from a quantized TFLite model of a sine function. I followed the same steps as I did about one year ago with X-CUBE-AI 8.1.0 (at the time it worked) but this time, when I perform inference, the network answers with constant output independent on its input. My steps are essentially these:- Create a project for my board (Nucleo F767ZI);- Add the X-CUBE-AI 9.0.0 pack and the TFLite model;- Accept all the optimizations that the IDE proposes;- Activate the CRC;- Generate code;- Add in the main() the code to create the network / set the activations, input and output buffers / run the network; The code is almost identical to the example contained in the X-CUBE-AI documentation.My development platform is a MacBook Pro with M3 chip and Sequoia OS. It is likely that I missed something, since one year passed and I possibly forgot to write down some details of the configuration, but I tried all the variations

I currently have the STM32H7 DISC0 and plan on getting the STM32N6 development board. What I can't seem to figure out is if it is possible to train a custom dataset model on my own in tensorflow then download it onto the STM32 for inference? I was looking at platforms like YOLOv11 and Efficientdet. Also, are there any detailed videos or instructions I can look at? Please advise. Thank you. Marcus

Hi,I am currently working with the nucleo-n657x0-q development board and trying to use the ST neural-ART NPU of the STM32N657X0 MCU. While I know that neural network models can be automatically mapped to the NPU through the STEdgeAI-core, I was wondering if there is any documentation on how I could manually program the NPU? Either through a library/API or through low level functions.Thanks in advance!

I am using the STM32N6570-DK board to run AI model on it. And I have modified the stm32n6.mpool file because the board actually have a 1‑Gbit Octo‑SPI flash memory, which is 128MB instead of 64MB.  But even if I have already adjusted it, the cubemx still shows "Used Flash:3.51MB(.00B over 64.00 MiB xSPI2)". Is there a problem and how I can fix it? By the way, I want to know that where I can get some examples about using AI on the STM32N6570-DK board, and the correct way to modify the files so that I can use the external memory on the board.

Hello STM Community,I have a question regarding my FCNN model. My goal is to feed input data into the model and obtain the corresponding output, but I haven’t been successful so far. Could you please assist me with this? Any guidance would be greatly appreciated!Model Layer Information:Stm32 Anlyze Show Graph: 

Hello, I'm writing my bachelor thesis regarding the deployment of the model inside the STM32, and I just want to use CubeIDE to run my own program. It takes in the image data through the UART, put the data inside the model input buffer and then do the LL_ATON_RT_Main(&NN_Instance_Default); and finally gives out the ourt put.I really did everthing I can, I already wrote the parameters generated by the Cube AI in side the External flash (exactly same place as the cubeAI report, which is 0x70000000 ). The model is from offical website:  mobilenet_v1_0.25_224_fft_int8.tflite .the file is from the Neural ART(cube AI) the origianl name is network_atonbuf.xSPI2.raw, I copypaste it and rename it to .BIN so that I can download it with the cube programmer instead of using the cmd lines or some python files of the Neural ART.After that, I run my program, below is my ST program ,It works without any "wornings or bugs",I can inspect the output by monitoring the float buffer[101]

I encountered two problems while learning the Extrapolate library:One of the issues is that I am not very clear about this extrapolation value, and I don't know if there is a problem understanding it. I have always believed that this extrapolated value is a predicted data value made by AI after reading the raw data of my sensor and predicting the trend of sensor changes. However, while studying, I found that this value seemed to need to be set by myself, which made me very confused.Another question that I derived from the first one is that I am not sure what kind of data should be included in Add Singal. In my personal understanding, if I have two columns of data, is the first column the predicted value I set myself? Is the second column my original sequence?Then I tried to search for information in comments and online, but I found that I still couldn't find more information about the Extrapolate library in this NanoEdge AI studio. If you know, can you help me solve these two confusion

Hi, I'm trying to test a model on the STM32N6 but stedgeai tool fails to analyse the model:ST Edge AI Core v2.0.0-20049TOOL ERROR: list index out of range I'm using X-CUBE-AI 10.0.0, STM32CubeN6 v1.1.0 and STM32CubeIDE 1.18.0.The model I'm trying to use is in attachment.I've also tried with STM32Cube.AI MCU Runtime mode but the error is the same.How can I fix the issue ? Thanks,Alexis Murzeau

"Can Cube.AI deploy Transformer models? To deploy the Transformer model, I removed the LayerNorm layers and replaced them with BatchNorm1d layers while fixing all dynamic dimensions. However, during deployment, I encountered the error: INTERNAL ERROR: 'NoneType' object is not iterable."  

Hi All,I am looking at a similar kind of https://www.aceinna.com/openimu simulator for inertia measurement(accelerator, gyro, magneto meeter) for character recognition.I did search in this forum https://community.st.com/t5/mems-sensors/is-there-any-simulation-software-available-for-imu-sensors-where/m-p/314540#M13158 but I could see any response. Advanced Thanks.

X-CBUE-AI无法导入ONNX模型，最新版的X-CBUE-AI不支持ScatterND 和 Range 这两个层

hi,my question is why i can't analyze my model about onnx and cubemx don't back type about my error.my model about onnx size is nearly 3mb,so the compression which i used is high.but the cubemx still can't analyze.emmm i hope their have a handsome guys can help me.i will very appreciate to you!

After I uploaded my own model on the ST Edge AI Developer Cloud platform, I further quantized and optimized my model. However, when I selected my own model and after a few minutes of analysis and identification, the platform could not identify the model's input, output, and model type, so that I could not proceed to the next step. If possible, please let me know the reason and help me solve this problem. Thank you very much. 

I am using the X-Cube-AI to deploy a neural network onto  a STM32N6570-DK board. I selected the "n6-allmems-O3" option, and after analyzing, it shows two data, labeled as used RAM and used Flash. I am wondering since I have selected the "allmems" option, the RAM usage refers to the usage of the external RAM or the internal RAM, and the Flash usage refers to the internal Flash or the external Flash.

I want to use the STM32F407 to implement the NanoEdge AI Library for Anomaly Detection (AD) tasks.The documentation mentions nanoedge.ai's method for backing up and restoring the library knowledge using the .ld file in a Linux environment. I want to know how to perform this backup operation in Keil.