Edge AI

Hi everyone,I encountered problems when deploying the neural network using STM32N6570-DK.I was deploying my model to the STM32N6570-DK development board according to the routine, but found that the code file obtained by optimizing the model using the Stedg-AI web version did not contain the hex file, that is, network_data.hex under the Binary file of any routine folder. Without this file, I cannot complete the deployment of the neural network according to the steps of the routine.I attempted to solve this problem by downloading the ST EdgeAI software version, but for the downloaded ST EdgeAI Core 2.1.0, I couldn't find its shortcut to open it. I thought it was a version issue and tried to download ST EdgeAI Core 2.0.0, but got stuck at the step of ST Neural-ART package. I clicked on the download link of ST Neural-ART package on the installation interface. However, it returned to the download interface of ST EdgeAI Core. The download resources of the ST Neural-ART package could not be f

I wanted to import a trained onnx into cuba.AI, but an error occurred. I have no idea where the problem lies. After troubleshooting, it was initially determined that there was an issue with the lstm part, but I have no way of knowing exactly where the problem is. I'm looking forward to your explanation. Here is my model codeimport torchfrom torch import nnimport torch.nn.functional as F# from torchsummary import summaryfrom torchinfo import summary## cnn extract featuresclass mfcn(nn.Module):def __init__(self:(super(mfcn, self).__init__()self.stage1 = nn.Sequential(nn.Conv1d(in_channels=1, out_channels=16, kernel_size=3, stride=1, padding=1, groups=1),nn.BatchNorm1d(16),nn.Conv1d(16, 16, 3, 1, 1, 2),nn.BatchNorm1d(16),nn.ReLU(True),nn.MaxPool1d(kernel_size=2, stride=2), # 16*320nn.Conv1d(16, 32, 3, 1, 1, 4),nn.BatchNorm1d(32),nn.ReLU(True),nn.MaxPool1d(2, 2), # 32*160nn.Conv1d(32, 64, 3, 1, 1, 8),nn.BatchNorm1d(64),nn.ReLU(True),nn.MaxPool1d(2, 2), # 64*80nn.Conv1d(64, 128, 3, 1, 1, 8)

Hi All,The STM32N6 series is still very new to me. I have some experience with other low power NPU enabled devices, however the main problem faced is the support of ultra-low lux camera modules; gaining access to datasheets, register maps and application notes is pretty much impossible.I am really impressed with ST offering with the STM32N6 as well as the great libraries, etc.With the x-cube-n6-ai-h264-usb-uvc example, I see UVC is used for streaming images to a host PC. I want to do the opposite. The idea being that either frames from the stream or images are capture and stored in external RAM for inference.Is this possible? Thank you

Initially I was having issues in generating the project file for ai models because of CUBE AI withe the latest update it got fixed. But while trying to build an flash the code to the Board there was issue while flashing the code the debugger was giving output as target is not responding. for checking there was no issue with the connection or debugger I tried creating a sample example from CUBE MX and while running that one it was working fine.I have given below the screen shot of the error that i was getting.Thanks and Regards,Vinay

I was successfully programming my STM32N6 disco board until today when I got this error:Board programming failed: "Error: binary file does not exist: Debug/STM32N6570-DK_GettingStarted_ObjectDetection.bin"I am uploading quantized tflite models to the board and all the switches are correctly populated on the board. The same thing happens with on_cloud: False or TrueAny ideas?thanks

你好，近期我在使用NanoEdge AI Studio实现异常检测功能，基于三轴加速度计的数据实现。我出现了以下问题：1.我使用的最新版本5.0.2，在benchmark界面训练模型后，表格Best Library Result 中异常数据的结果为零，就是在表格的底部。但我生成代码需要的文件后运用到工程中其相似度一直输出为91%，即使我大幅度的改变加速度计的值也仍然时91%。2.在有一次训练时，表格Best Library Result 中异常数据的结果不为零了，大概为68%。我生成需要的额NanoEdge AI.h和library.a以及knowledge.h生成后运用到keil工程中发现能够正常进行学习，并且也可不学习使用knowledge.h的这个数据集进行检测相似度，结果较为明显。只是原本的异常数据其相似度对应的也为68%。3.我的疑惑在于，对应的模型训练不应该异常数据接近为零，正常数据接近一百吗，可我似乎于认为的不一样。并且同样的数据进行第一次训练的结果可能异常数据不为零，但后面几次训练后一直为零。导致的结果就是，训练结果异常数据为零的模型，在使用官方给的先进行学习一段时间，在进行异常检测运行效果是错误的，相似度一直输出为一个固定值，比如91%。并且运行不事先学习，利用knowledge这个数组作为学习数据，运行结果也是不正确的，但是训练结果异常数据不为零的就可正常运行。但这个结果也不是一直会出现的，偶尔出现。我的数据上传使用的是USB串口上传（USB转TTL、或者蓝牙串口） 以下为代码（省略了部分）#include “NanoEdgeAI.h”#include “knowledge.h”#define LEARNING_ITERATIONS 30浮点数 input_user_buffer[DATA_INPUT_USER * AXIS_NUMBER];输入值的缓冲区无效 SystemClock_Config（void）;静态 void MPU_Config（void）;void fill_buffer（float input_buffer[]）{/* 用户开始 */float acc_mg[3]; // 临时存储单个样本的X/Y/Z数据uint16_t samples_collected = 0;uint32_t last_tick = HAL_

Hello,I am working on a NUCLEO-N657X0-Q and i'm trying to fill the runtime input buffer of an NPU accelerated miniresnet model with random data in order to verify that it is working, however, the buffer remains unaffected and filled with zeroes.Do I have to do something different before/after filling (like invalidate some kind of cache?)I am attaching a snippet of the code and the UART output aswell as the model analysis text.I am using the external flash to store the model @ 0x71000000 (n6-extflash profile). The npuRAM5 (0x342e0000) is also used and project is setup as LRun.The base code was generated by STM32CubeMX GUI ThanksTasos  

Hi,I'm trying to test a model(Shallownet+LSTM) on the STM32F4, but the tool fails during analysis:ST Edge AI Core v2.1.0-20194TOOL ERROR: list index out of range I ran the tool with debug enabled: export DEBUG=2And I observed in the log:Created input transpose layer: _lstm_LSTM_output_0_forward_in_transposeError in execution of pass type(OPT_TRANSPOSE_LSTM) id(80) The model and code are included in the attachment.How can I fix the issue ?Thanks,Joker_Han 

Hello, I'm writing my theorical part of my Bachelor Thesis. I'm interesed in whether two output ports from two Processing Unit (for example) the Convolution accelerators can be connected to one input port of the POOL, is it possible to do this through the Stream switch? So that we can have two CAxs to work together parallel for the pooling. From the documentation I cant find a specific answer.And what is the reason here to divide the convolution accelerators as CA0 and CA1?????????????????????????????????????????????????? is that because the can work parallel for one pooling as I mentioned before ????????????????????????Thanks a lot in advance, I really need an answer 

i followed the instructions or steps listed out in below ticket Which software do I need to install to start with STM32N6570-DK?Currently I am struck at this error please help me.Regards,ASR

Hi everyone,I’m currently working with the MB1854 ST Camera module, which uses the Sony IMX335 CMOS sensor. I’ve managed to find the datasheet for this sensor.At this point, I’ve successfully changed the image resolution from the default 2592x1944 to 256x256 using the window cropping mode. However, I’m currently stuck on how to change the frame rate.On page 56 of the datasheet, it mentions:I know the value of V_TTL, but I’m confused about the value of the 1H period. There is no "Operating Mode" table in the datasheet. I did find the Read Drive Mode table, which includes values for the 1H period, but they do not apply to my mode (Window Cropping) and appear to be fixed.Additionally, there’s no mention of a formula for calculating this value.Also, this datasheet appears to be version 0.2, which was last updated in 2017. I'm not sure if this is even the latest version. I tried to find a forum specifically for Sony camera sensors but couldn't find one.Could someone please help clarify this

Hello, we encountered the following issues when deploying a binary classification model using the ll library. Do you have any solutions?1. The output data of the AI model deployed according to the example (without quantization) does not match the data calculated locally on the PC (before softmax).2. We compared the model calculation results on the PC with those on the STM32N6 layer by layer and found that the mismatch in the calculation results before and after reshape (in the underlying code module of DMA to NPU) led to the mismatch in the final output results. The calculation results of other layers such as conv, maxpool, and relu were all correct.3. How should we solve the model deployment?

Hello!    I plan to use NanoEdge AI Studio for sound anomaly detection, but currently I have some confusion: the resources of embedded chips are limited, and the amount of sound signal sampling data is relatively large. Can online learning be implemented on hardware. If feasible, the chip should not be able to learn much sample size. Can this ensure high accuracy.    Can you help me solve the problem.Thank you.

 Hello!I'm using NUCLEO-H7S3L8 and trying to implement my AI on the external octo flash using STM32CubeMX and X-Cube-AI. How can I do this? My main application is already running in the octo flash.Thanks,Marcos. 

 Hello everyone,I encountered an issue while using ST Edge AI 2.1.0 and I would appreciate some help.Here is my original CUSTOM_MPOOLS configuration, which worked perfectly: "CUSTOM_MPOOLS": { "memory_pool": "./my_mpools/MY_CUSTOM_MPOOL.mpool", "options" : "--enable-epoch-controller -O3 --all-buffers-info --mvei --cache-maintenance --native-float --enable-virtual-mem-pools --Omax-ca-pipe 4 --Ocache-opt --Os" } However, when I added the following options to the options section: --input-data-type uint8 --inputs-ch-position chlast --output-data-type float32  I received the following error: [AI:network] E103(CliRuntimeError): Error calling the Neural Art compiler - ['', 'The following arguments were not expected: float32 --output-data-type chlast --inputs-ch-position uint8 --input-data-type', 'Run with --help for more information.', ''] I referred to the official documentation and saw that it does mention using these commands to modify

When I generate a model's C code with Edge AI DC, what files do I need to replace in this application code example: https://github.com/STMicroelectronics/stm32ai-modelzoo-services/tree/main/application_code/image_classification/STM32N6 to get the model running? Also do I need to still follow steps 1 & 2 since the model is already generated with Edge AI DC? 

I'm not getting the option to generate a CubeIDE project with the Edge AI DC despite documentation listing that as an option. I'm only getting the option of downloading the C code 

Hi,I am currently working with the nucleo-n657x0-q and trying to validate a tflite model on it. While it works in the neural-ART runtime I cant seem to get a validation program working for the MCU runtime. For the neural-ART runtime I followed the NPU_Validation guide from the 2.1 ST Edge projects package, this worked without problem.For the MCU runtime, my first approach was to follow the guide mentioned above but omit the --st-neural-art flag for the ST cli generate command, when running the n6_loader.py script this results in a "Compilation failed". Attached are the logs produced with the error.My second approach was to use the cubeMX to generate a validation program. The main issue here is that each time that I enable validation in the X-cube-ai package the software freezes 99% of the time. I have been able to work around this issue by first creating a template program in MX and then changing to validation and regenerating the code. What I am stuck on now is that I do not know

 Hello everyone,I am using CubeAI 10.1.0 and STAGEAI 2.1 to analyze my model for STM32N6, and I encountered an issue where some epochs show ?? instead of the expected results. Here's the log:epoch ID HW/SW/EC Operation (SW only)epoch 1 ECepoch 2 ECepoch 3 -SW- (DequantizeLinear)epoch 4 -SW- (PRelu)epoch 5 -SW- (QuantizeLinear)epoch 6 -SW- (MaxPool)epoch 7 ECepoch 8 ECepoch 9 -SW- (DequantizeLinear)epoch 10 -SW- (PRelu)epoch 11 -SW- (QuantizeLinear)epoch 12 ECepoch 13 ECepoch 14 -SW- (DequantizeLinear)epoch 15 -SW- (PRelu)epoch 16 ECepoch 17 -SW- (Conv)epoch 18 -SW- (Add)epoch 19 ECepoch 20 -SW- (Conv)epoch 21 -SW- (Add)epoch 22 ??epoch 23 -SW- (Add)epoch 24 ??epoch 25 -SW- (Add)epoch 26 ECIn epoch 22 and epoch 24, the result is shown as ??, and I couldn't retrieve any computation results. I have a few questions:1. What does ?? mean?Does the ?? represent that some operators or operations failed to execu

HI! I am newbie to CUBE AI. I m working on implementing my CNN network that takes input image of 128,128,1 (greyscale) and give an output of 3 classes {class1,class2,class3}.i did convert and try my model to TFLITE successfully on python. then i went to stmcube and did put the model using X-CUBE-AI application template .my problem is with    int acquire_and_process_data(ai_i8* data[]) { /* fill the inputs of the c-model for (int idx=0; idx < AI_NETWORK_IN_NUM; idx++ ) { data[idx] = .... } */ return 0; }  my input buffer is like this  #define AI_NETWORK_IN_1_FORMAT (AI_BUFFER_FORMAT_FLOAT) #define AI_NETWORK_IN_1_HEIGHT (128) #define AI_NETWORK_IN_1_WIDTH (128) #define AI_NETWORK_IN_1_CHANNEL (1) #define AI_NETWORK_IN_1_SIZE (16384) #define AI_NETWORK_IN_1_SIZE_BYTES (65536)  i tried putting image as image [128*128] and image[128][128]

Hello everyone,I am currently working on a gesture recognition project using ST's VL53L5CX multi-zone laser distance sensor , and I would like to ask if anyone in the community has experience with related gesture datasets or has worked on similar applications.So far, I have been able to obtain multi-zone distance data from the sensor using NUCLEO + VL53L5CX in an Arduino environment. However, to train and validate the gesture recognition algorithm, I need to collect a large number of gesture samples.Therefore, I would like to ask:1、Is there any publicly available VL53L5CX gesture dataset ?2、Has anyone worked on similar projects and is willing to share some experience or methods for data collection?3、Are there any recommended approaches for feature extraction, preprocessing, or classification algorithms for such gesture data?If anyone in the community is interested in sharing raw data or collaborating on building a dataset, I would be more than happy to work together on this!Thank you a

Hi,I'm trying to implement "1 - x" computation as a part of implementing GRU using onnx model format. I tried doing Sub(1, x), but the Sub operator is not accelerated on the NPU and is run on in software instead.I've attached the model "single_sub_rewritten_q.onnx" for this :(const_1 is a vector of all 1 here, but using a single scalar of 1.0 does the same).And this is the output of stedgeai which shows that Sub is running outside the NPU:Epochs details --------------------------------------------------------------------------------- Total number of epochs: 1 of which 1 implemented in software epoch ID HW/SW/EC Operation (SW only) epoch 1 -SW- ( Sub ) ====================================================================================   Then I tried to do Add(1, Neg(x)) instead, the resulting model is accelerated on the NPU, but the output of the NPU is wrong and always the constant 127 according to the validation output.Running the validation gives this results:

In the Cube AI documentation (stneuralart_api_and_stack.html) there is this code block /* Wait for next event */ if (ll_aton_rt_ret == LL_ATON_RT_WFE) { /*** subject to change to fit also user code requirements ***/ LL_ATON_OSAL_WFE(); }to wait for an event while an epoch is running. I looked into what WFE means and it turns out that it puts the CPU in a low power state and crucially gates all the clocks. The effect of this is that the 4 timers I have running generating PWMs all start getting delayed. I can see on the scope the period increasing up to 4-5x.Is there another way to wait for the epoch to finish without going into a low power state? I am not currently using an RTOS but will switch to one later.

Hi all,Just curious if anyone here is experimenting with stepper motors controlled by Edge AI systems (like Raspberry Pi + AI accelerators, Jetson Nano, etc.). I'm exploring the idea of using local AI models for adaptive motor control — for example, adjusting speed or torque in real time based on sensor input, without relying on a cloud connection.Some use cases I’m thinking of:Smart 3D printers that self-optimize print parametersIndustrial robots with more responsive motor controlPredictive maintenance based on motion patternsHas anyone tried this? Any tips on frameworks or libraries that work well for real-time AI + motor control?Would love to hear your thoughts or see your projects!

greetings!Continuing my studies on MCU STM32, I am dedicating myself to STM32N6.I need to learn how to develop an application that in its first stage will monitor noise and classify it with a neural network type CNN. I have already made a simulator with Python, and a converter of the model's H5 file to tinyML, thus creating a header file (.h) with the neural network model. Now I need to learn how to put it on the stm32N6 and thus classify the noises captured with the STM32N6-DK microphone.In the second stage, I need to develop a CV (Computer Vision) to identify the silhouette of an object and center it, the equipment will be installed on a rover with omni wheels that will move to position the audio pickup prepared in the first stage.I looked at the examples that come with cubemx, but I didn't find anything related to the use of CV and NN.Could you tell me where I can get more advanced examples for STM32N6 that use computer vision and neural networks, especially CNN? Thanks for the