COMPATIBILITY SYSTEM IN ARTIFICIAL INTELLIGENCE MODEL TRANSFORMATIONS
Patent Information
- Application Number
- TR202609299
- Authority / Receiving Office
- TR · TR
- Patent Type
- Applications
- Current Assignee / Owner
- Filing Date
- 2026-06-11
- Publication Date
- 2026-06-22
Smart Images
Figure 00000021_0000
Abstract
Description
1 TARIFF COMPATIBILITY SYSTEM IN ARTIFICIAL INTELLIGENCE MODEL TRANSFORMATIONS Technical Area This invention enables artificial intelligence models to be used in mobile devices, smart cameras, and in-vehicle applications. so that it can be run on systems and electronic devices with embedded processors The output produced by the model after the transfer operations are completed. location information, coordinate information, classification results and data included in the report 10 in the ordering and / or positioning of the detection results It relates to a system that enables the identification of differences. Previous Technique Today, artificial intelligence models are capable of image recognition, object detection, face detection, and 15 To perform classification operations in a computer environment by being trained on smartphones, in-vehicle electronic systems, security cameras and It is run on electronic devices that contain artificial intelligence processors. In order for a trained artificial intelligence model to run on these devices, the model must... Model 20 was created in the software environment where the training processes were carried out. the file, which can be read by the device's artificial intelligence infrastructure, It needs to be converted into a different, executable model format. For example... A source model trained in a PyTorch or TensorFlow environment, an open neural network. to the exchange format (ONNX - Open Neural Network Exchange) and then Deep learning container (DLC-25) that can be used on devices with Qualcomm processors. It can be converted to Deep Learning Container format. However, this conversion... The way data is stored during processing, tensor (multidimensional data array) dimensions, axis sequences, channel layouts, and output layer structure by changing the model that produces accurate results in a computer environment, Qualcomm device. The model being worked on may yield different results. Therefore, artificial intelligence 30 obtained in a computer environment where the training and testing processes of the model are carried out 2 The results obtained and the model run on a Qualcomm processor device. Identifying the differences that arise between the results obtained as a result, Identifying the source of the differences and the results obtained on the device. shortcomings have been identified in producing it at the expected level of accuracy. It is coming out. 5 Therefore, considering the studies and shortcomings in the current technique... When equipped, it features image processing, face detection, object detection, and in-vehicle camera capabilities. artificial intelligence models used in systems on different devices After the model transformation processes performed in order to make it run, 10 is obtained. Identifying potential data structure differences and interpreting these differences in the model Location information, classification results, and information about objects generated by the system. to analyze whether it leads to misinterpretation of other outputs and results corresponding to the results obtained in the environment where the model was first developed a 15 that enables the creation of data processing rules necessary for production. It is understood that the system is needed. United States Regulation US2020410354A1, which is included in the known state of the art. The patent document describes artificial neural networks in different processing environments. calculation errors that occurred during execution and output 20 The text refers to a system that enables the detection of discrepancies. The subject of the invention is to use a sample input from a neural network on a reference processor. creating reference tensors and running the same neural network on the target processor It produces device tensors by operating it. The produced device tensors are used as reference. 25 is determined. If a mismatch is detected, the number of neural network layers is determined. The network is gradually shortened by reducing it, and each shortened network is recompiled. New device tensors are obtained by running the device. Throughout this process, the device By comparing the tensors with the reference tensors, the shortest nerve where the error persists is identified. The network is determined. Then, the lower-level intermediate 30 for this abbreviated neural network is determined. Tensor outputs are enabled in the representations. The compiler enables the machine. 3 Thanks to the instructions added to the commands, the tensors belonging to the intermediate layers can also be exported. can be transferred. The resulting intermediate-level device tensors are then transferred to the corresponding reference. By comparing the tensors, the last matched tensor is identified along with the first mismatched tensor. This is done. Thus, the layer where the error occurred, the intermediate representation level, or The calculation step can be defined. 5 Brief Description of the Invention The goal of this invention is to develop AI technologies similar to PyTorch, TensorFlow, and Keras. Converting models created in these environments to ONNX and DLC formats 10 By analyzing the output data obtained afterwards, the data generated by the model location information, coordinate information, classification results and detection results Identifying differences in ordering and / or positioning The goal is to create a system that provides this. Another purpose of this invention is the software in which the artificial intelligence model is created. The model uses output data from its environment, mobile devices, smart cameras, and in-vehicle systems. or run on electronic devices with embedded processors By comparing the resulting output data, object position, face position, 20 in the ordering of object coordinates, classification results and analysis results. and identify the differences in positioning and how these outputs affect the device. A system that allows determining its position and location. to accomplish. Another purpose of this invention is the software in which the artificial intelligence model is created. 25 Output data obtained in the environment and data obtained on electronic devices The order, position, axis arrangement, channel order, and other criteria determined among the output data are as follows: reordering output data by exploiting differences in data placement and automatically generates rules that enable rearrangement, 30 that apply the rules to the output data and are obtained on electronic devices 4 the outputs will correspond to the output pattern obtained in the software environment The goal is to create a regulating system. Detailed Description of the Invention The "Artificial Intelligence Model" was developed to achieve the purpose of this invention. The "Compatibility System in Transformations" is shown in the attached figure; Figure 1 shows a schematic view of the system that is the subject of the invention. The parts shown in the figure are individually numbered, and these numbers correspond to... The corresponding answers are given below. 1. System 2. Electronic Device 15 3. Application 4. Database 5. Server Y. Artificial Intelligence Model Source Artificial intelligence models are being used in mobile devices, smart cameras, in-vehicle systems, and so that it can be run on electronic devices with embedded processors The output produced by the model after the transfer operations are completed. location information, coordinate information, classification results and data included in the report 25 The system in question, developed to enable the identification of differences, is the subject of the invention. (1); - exchanging data using any remote communication protocol at least one electronic device configured to perform (2), - It is being run on electronic device (2) and the input of the artificial intelligence model is 30 images, video data, test data, model-generated object location, surface location, coordinate information, classification results, and obtaining, sharing, and distributing output data containing the detection results. at least one configured to be displayed by the user application (3), - contains input data, output data, and test 5 of the artificial intelligence model. data, comparison data, difference analyses and the data created at least one configured to store records relating to conversion rules database (4), − Communication with application (3), database (4) and artificial intelligence model source (Y) 10 obtained in the software environment where the artificial intelligence model was created by establishing output data and output data obtained from electronic devices (2) compare object position, face position, within the output data. coordinate information, classification results and detection results differences in their ordering, positioning and arrangement to determine, re-analyze output data according to identified differences 15 to create transformation rules that will enable its regulation It includes at least one configured server (5). The electronic device (2) in the system (1) which is the subject of the invention, any remote communication using the protocol, it automatically processes data and creates meaningful 20 smartphones, tablets that can run applications and / or software that produce results computers, laptops, desktop computers, cameras, vehicle electronics It is a device that has hardware and / or an embedded processor. The application (3) in the system (1) which is the subject of the invention, any remote communication 25 Communication established between the server (5) and the electronic device (2) using the protocol It is configured to exchange data with the server (5) via. Application (3) runs on electronic device (2) and is based on artificial intelligence model input images, video data, test data, and data generated by the model. object location, face location, coordinate information, classification results and detection 30 obtaining the output data containing the results and transmitting it to the server (5) 6 It is structured to provide. Application (3), artificial intelligence model model files, test data and reference output obtained from source (Y) to ensure that the data generated by the server (5) is transferred to the target system comparison results, output differences, and the conversion generated 5. It is structured to offer a minimal interface. The database (4) in the system (1) which is the subject of the invention, is obtained through the application (3). AI model files, model weights, inputs images, video data, test data, zero-value test inputs, fixed test 10 data, random test data, boundary case test inputs, anonymous test images, reference output obtained in the software environment where the model was created to store data and output data on electronic devices (2) It is structured. The database (4) contains the object within the output data. location, face location, coordinate information, classification results and identification 15 Records relating to the results; order, position, axis arrangement, channel of output data. Ordering, data placement, layer and output names, color space information, normalization parameters, comparison data, difference analyses, compatibility test results, conversion rules, performance metrics, and compliance reports to store, comparison of these records will be performed by the server (5), 20 in difference detection, conversion rule creation and validation processes It is configured to be shared with the server (5) for use. The server (5) in the system (1) which is the subject of the invention, any remote communication application (3), database (4) and artificial intelligence model 25 using the protocol to communicate with source (Y) and exchange data It is being configured. The server (5) receives the artificial intelligence model from the source (Y). and image processing, face detection, object detection, driver monitoring operations Artificial intelligence platforms like PyTorch, TensorFlow, and Keras are used to achieve this. model files of artificial intelligence models created in development tools, 30 model containing parameter values obtained during the learning process 7 weights, input information defining the input data to be given to the model, model output information that defines the output data to be produced by the model, within the model the steps included, image and video taken from the electronic device (2) data, the software environment in which the model was created, the ONNX intermediate model, and artificial intelligence SNPE 5 enables the intelligence model to be run on an electronic device (2). (Snapdragon Neural Processing Engine) producing the same results on the DLC model executed by the software zero-value test inputs used to check that it does not produce anything, model The numerical data at the inputs and outputs are represented as multidimensional arrays. tensor (multidimensional data set) data, randomly generated test 10 test data including boundary state test inputs and anonymous test images data and object position obtained in the software environment where the model was created, face location, coordinate information, classification results, and detection results. The server (5) is configured to receive the reference output data containing. The object position in the software environment where the artificial intelligence model was created, face 15 including location, coordinate information, classification results and detection results. Reference output data obtained from the model converted to ONNX format comparing the output data and identifying the differences between the two output data points. to identify differences during the conversion of the model to ONNX format The model, which is determined to have appeared or not and converted to ONNX format, is 20. to determine whether it produces results consistent with the reference output data is configured. The server (5) provides results that are consistent with the reference output data. The ONNX model, which was determined to have been produced, is an artificial intelligence model used in electronic devices. (2) DLC that can be used by SNPE software which enables it to be run convert to format, transfer the converted model to electronic devices (2) and 25 to enable the model to be run on electronic devices (2) It is configured. The server (5) is in DLC format of the ONNX model. Tensor changes in the model's input and output data during conversion (multidimensional data array) dimensions, tensor axis ordering, data the arrangement of channels, the order of output layers, the memory location of output data 30 its layout within, resulting from the reshaping of the output data. 8 resulting from alignment changes and axis shift operations It is structured to identify the differences. Server (5), electronic Image and video data received from the device (2) are processed by OpenCV (Open Source Computer Using Vision Library - Open Source Computer Vision Library processing of the data, input 5 accepted by the artificial intelligence model. conversion of resolution, color space and channel sequences into the model input normalization of pixel values, making them conform to the definitions and the image data can be processed by the model. Software that creates the model by enabling its conversion into tensor data. 10 Use of the same input data in the environment and on the electronic device (2) It is configured to provide the server (5) zero-value test inputs. Example test of multidimensional datasets used in model inputs and outputs. data, randomly generated multidimensional datasets, boundary case tests using inputs and anonymous test images, the artificial intelligence model in the software environment in which it was created, on the ONNX intermediate model and on the DLC model 15 to ensure it runs with the same input data, the resulting object position, face including location, coordinate information, classification results and detection results. comparing output data, identifying the information contained within the output data. sequence, position, axis placement, channel arrangement, multidimensional data array size, and data. to determine the differences that emerge in the settlement and the differences in the output data 20 It is structured to analyze the effects on its interpretation. Server (5), As a result of the analyses, the source model output and the electronic device (2) were obtained The output data includes data order, data position, and axis placement. channel arrangement, output segmentation, flattened output layout, and grid. by evaluating differences in output patterns based on a specific 25 in the source model Object position, face position, coordinate information, in a given row or location. classification result and detection result data on electronic device (2) The output data obtained includes the sequence, position, channel information, and to generate intermediate mapping information showing data placement It is structured. Server (5), 30 created as a result of comparison and analysis. Using the interpolation information, determine the reasons for the differences between the output data. 9 to determine the differences between the dimensions of the multidimensional data set from, to, the shifting of data axes, to, to, the channel ordering from changes, from the generation of output data in different sections, from the data from the arrangement of value distributions in a smoothed or grid structure scale 5 resulting from change and quantization processes from differences or different interpretations of coordinate information It is configured to determine whether or not it originates from a source. Server (5), obtained on the source software environment, ONNX intermediate model and DLC model semantic output shows the semantic correspondences between the obtained output data. By creating a mapping matrix (semantic output mapping matrix), an object or 10 data indicating the position of a face within an image, pertaining to an object or face. Coordinate information is used to classify which class the object belongs to. the results, the detection results obtained by the model, the detection made confidence values indicating the level of accuracy of the detected object or face Information about the border box showing the boundaries within the image and the face or object 15 The marker point information regarding the reference points determined on it is different. It is structured to identify the corresponding values within the output data. The server (5) determined the output as a result of the comparisons and analyses performed. The output was generated by using the correlations and reasons for differences between the data. Data reshaping, which is necessary for the reorganization of data, 20 Axis shifting, data merging, data splitting, index matching, channel sorting and to create coordinate manipulation rules and to use those rules source software of the output data obtained on the electronic device (2) to ensure that the output obtained in the environment is adapted to the standard. is configured. Server (5), generated encoder conversion 25 rules, parameters obtained by the artificial intelligence model during the learning process model weights containing the values and the processing steps included in the model without changing the model's input data or the output data produced by the model to apply on, object position obtained on electronic device (2), face 30 including location, coordinate information, classification results and detection results. output data obtained in the software environment where the model was created to ensure that it is rearranged in a way that carries the same meaning is being configured. Server (5), Automated Testing Suite with zero-value test inputs, multidimensional inputs and outputs used for model inputs and outputs. sample test data for datasets, randomly generated test data, Using real image samples and boundary case test inputs, the model's 5 in the software environment in which it was created, on the ONNX intermediate model and the DLC model. comparing the obtained output data again, the generated encoder transformation object position obtained before and after the rules are applied, face location, coordinate information, classification results, and detection results. Measuring the differences between them, the extent to which the output data are consistent with each other. 10 the error rate, the time it takes for the model to generate results, and the matching of outputs. It is configured to calculate the level and difference limits. Server (5), multidimensional data used during the study for model optimization the sizes of the arrays, the way input and output data are stored in memory, etc. the arrangement of 3-dimensional data arrays in memory, memory alignment scheme, 15 data type conversions, quantified and floating-point data representation formats and the model's CPU (Central Processing Unit), GPU (Graphics Processing Unit) if run on a Digital Signal Processor (DSP) or Unit by analyzing the resulting processing time, memory usage and performance values The model has low latency, low memory consumption and 20 on the electronic device (2). to ensure it operates with consistent performance in producing results It is structured. Server (5), software in which the artificial intelligence model is created. the environment, the ONNX intermediate model and the output data obtained on the DLC model Comparison and difference analysis to determine the level of compatibility between them. Output matching, encoder conversion rule creation, compatibility testing, and performance 25 By evaluating the results of the measurement processes, the model is an electronic device (2) compatibility for reliable and reliable operation on the system. to generate information and reports, and to transfer the said data to the database (4) transfer and make it viewable by the user via the application (3) It is structured to provide. 30 11 Industrial application of the invention Thanks to the system (1) that is the subject of the invention, artificial intelligence models can be used with PyTorch, TensorFlow, Keras, or similar development environments can be used for mobile devices and smartphones. camera systems, image processing applications, automotive systems, objects 5 Internet of Things (IoT) devices and artificial intelligence inference on devices data structure created during transfer to embedded systems that perform this function Inconsistencies are identified and resolved, and after model transformation, the input and The output data is adapted to the data format expected by the target device. Model integration processes are simplified, and models can be implemented on target devices in 10 to produce outputs corresponding to the results obtained in the educational environment It is ensured that it is operational. Around these fundamental concepts, the subject of the meeting is "Artificial Intelligence Model Transformations". It is possible to develop a wide variety of applications related to the Compliance System (1)” 15 and the invention cannot be limited to the examples described here, but mainly to the claims as stated.
Claims
12 REQUESTS 1. Artificial intelligence models in mobile devices, smart cameras, in-vehicle systems, and so that it can be run on electronic devices with embedded processors The output produced by the model after the transfer operations are 5. The data includes location information, coordinate information, and classification results. and the ordering and / or positioning of the detection results Developed to enable the identification of differences; - exchanging data using any remote communication protocol at least one electronic device configured to perform (2), 10 - It is being run on an electronic device (2) and the input of the artificial intelligence model images, video data, test data, model-generated object location, surface location, coordinate information, classification results, and obtaining, sharing, and distributing output data containing the detection results. at least one 15 configured to be displayed to the user application (3), - contains input data, output data, and test data belonging to the artificial intelligence model. data, comparison data, difference analyses and the data created at least one configured to store records relating to conversion rules containing database (4) and 20 − Communication with application (3), database (4) and artificial intelligence model source (Y) obtained in the software environment where the artificial intelligence model is created by establishing output data and output data obtained from electronic devices (2) compare object position, face position, within the output data. coordinate information, classification results and detection results 25 differences in their ordering, positioning and arrangement to determine, and to re-evaluate the output data according to the identified differences. to create transformation rules that will enable its regulation a system characterized by having at least one server (5) configured (1). 13 2. Automatically transmitting data using any remote communication protocol. applications and / or software that function and produce meaningful results capable of running; smartphone, tablet computer, portable computer, desktop computers, cameras, vehicle electronics equipment and / or embedded processors A system like the one in Claim 1, characterized by an electronic device (2) (1). 5 3. Electronic communication with the server (5) using any remote communication protocol. Data exchange with the server (5) via communication established between the device (2) characterized by the application structured to perform (3) a system like any of the above requests (1). 10 4. The electronic device (2) is running and the input to the artificial intelligence model images, video data, test data, model-generated object location, surface location, coordinate information, classification results, and Obtaining the output data containing the detection results and sending it to the server (5) 15 characterized by the application (3) which is structured to enable transmission. a system like any of the above requests (1).
5. Model files and test data obtained from the AI model source (Y). and ensure that the reference output data is transferred to the target system, server (5) 20 comparison results, output differences, and produced by Information regarding the conversion rules created is provided by the user. configured to provide at least one interface that allows it to be displayed any of the above claims characterized by application (3) such a system (1). 25 6. Model files of the artificial intelligence model obtained through application (3), model weights, input images, video data, test data, zero valuable test inputs, constant test data, random test data, boundary cases test inputs, anonymous test images, software used to build the model 30 14 Reference output data obtained in the environment and in electronic devices (2) characterized by the database (4) which is structured to store the output data. a system like any of the above-mentioned requests (1).
7. Object location, face location, coordinates included in the output data. records relating to information, classification results and detection results; output data order, position, axis arrangement, channel ordering, data placement, Layer and output names, color space information, normalization parameters, comparison data, difference analyses, compatibility test results, conversion storing these rules, performance metrics, and compliance reports, these 10 Comparison and difference of records will be performed by server (5) Used in identification, conversion rule creation, and validation processes. database (4) configured to be shared with server (5) a system like any of the above characterized claims (1). 15 8. Using any remote communication protocol, the application (3), data communicate with the base (4) and the artificial intelligence model source (Y) and data characterized by the server (5) configured to carry out the transaction. a system like any of the above requests (1). 20 9. Image processing, facial recognition, obtained from artificial intelligence model source (Y). to perform detection, object recognition, and driver monitoring operations In AI development tools such as PyTorch, TensorFlow, and Keras model files of created artificial intelligence models, the model's learning 25 model weights containing the parameter values obtained during the process, added to the model input information that defines the input data to be provided by the model The output information, which defines the output data to be produced, is included in the model. the steps of the process, image and video data received from the electronic device (2), The software environment in which the model was created, the ONNX intermediate model, and artificial intelligence 30 SNPE software that enables the model to be run on an electronic device (2). whether it produces the same results on the DLC model run by zero-value test inputs used for verification, model input and The numerical data at the outputs are represented as multidimensional arrays. tensor data, randomly generated test data, boundary state test 5 test data including inputs and anonymous test images, and the model object position, face position obtained in the software environment in which it was created, Reference containing coordinate information, classification results and detection results. Characterized by the server (5) configured to receive the output data a system like any of the above requests (1). 10 10. Object position and face in the software environment where the artificial intelligence model was created. location, coordinate information, classification results and detection results The model, which includes reference output data, is converted to ONNX format. Comparing the obtained output data, the 15 differences between the two output data points Identifying the differences, transferring the differences to the model's ONNX format. Identify whether it appeared during the conversion process and ONNX The model, converted to the specified format, yielded results consistent with the reference output data. characterized by the server (5) configured to determine whether it produces or not. a system like any of the above requests (1). 20 11. The ONNX model, which was determined to produce results consistent with the reference output data, enabling the artificial intelligence model to be run on electronic devices (2) Converting to a DLC format usable by SNPE software, transferring the converted model to electronic devices (2) and the model's electronic 25 Server (5) configured to enable operation on devices (2) a system like any of the above-mentioned claims characterized by (1). 16 12. During the conversion of the ONNX model to DLC format, the model's input and output... the tensor dimensions occurring in the output data, the tensor axes the order, the arrangement of data channels, the order of output layers, the output the placement of data in memory, the reorganization of output data the changes in arrangement and axis displacement resulting from shaping 5 server configured to identify differences arising from operations (5) like any of the above-mentioned claims characterized by system (1).
13. Image and video data received from the electronic device (2) in OpenCV 10 processing using, acceptance of the artificial intelligence model of the data in question converting it to the input resolution, color space and channel arrangements making the model conform to the input definitions, pixel values normalization processes are carried out and the image data is modeled By enabling the conversion of tensor data that can be processed by the model, 15 the same input on the software environment and electronic device (2) where it was created server (5) configured to enable the use of data a system like any of the above characterized claims (1).
14. Zero-value test inputs, multidimensional models used in input and output. Sample test data relating to data sets, randomly generated multidimensional data arrays, boundary condition test inputs, and anonymous test images In the software environment where the artificial intelligence model is created using ONNX, 25 to provide, obtained object position, face position, coordinate information, Output data including classification results and detection results. compare, the order in which the information is located within the output data, position, axis placement, channel arrangement, multidimensional data array size, and data. to determine the differences that emerge in the settlement and the differences in output 30 17 structured to analyze the effects on the interpretation of the data any of the above requests characterized by the server (5) such a system (1).
15. As a result of the analyses, the source model output and the electronic device (2) are 5 The output data includes data order, data position, and axis. layout, channel arrangement, output segmentation, flattened output layout and By evaluating the differences in grid-based output layouts, specific features are identified in the source model. the position of an object in a row or location, face position, coordinates information, classification result and detection result data of electronic device (2) 10 The sequence, position, and channel of the output data obtained on it. to generate intermediate mapping information showing the information and data placement from the above requests characterized by the configured server (5) a system like any other (1).
16. Intermediate matching information generated as a result of comparisons and analyses. using the term The issue is that the differences stem from the varying dimensions of the multidimensional data set. from shifting data axes, changing channel order, From generating output data in different sections, the data is flattened into 20 or from the arrangement in a grid structure, from the change in value distributions, scale differences arising from quantification processes or coordinates whether it stems from a different interpretation of the information The above is characterized by the server (5) configured to detect it. a system like any of the requests (1). 25 17. The source software environment was obtained on the ONNX intermediate model and the DLC model. semantic output shows the semantic correspondences between the obtained output data. By creating a mapping matrix, you can identify an object or face within an image. data indicating location, coordinate information of the object or surface, 30 18 The classification results, which show which class the object belongs to, are presented by the model. the accuracy of the findings of the investigation carried out by confidence levels indicating the level of accuracy of the image of the detected object or face. border box information showing the boundaries inside and faces or objects The marker point information regarding the reference points determined on it is different 5 Server configured to identify corresponding entries within the output data. (5) like any of the above-mentioned claims characterized by system (1).
18. The output data determined as a result of the comparisons and analyses performed are 10. By using the reciprocal relationships and reasons for differences between them, the output Data reshaping required for data reorganization, axis shift, data merging, data splitting, index matching, channel to create the sorting and coordinate arrangement rules and the aforementioned 15 output data obtained on electronic device (2) using the rules making it compatible with the output format obtained in the source software environment The above is characterized by the server (5) configured to provide a system like any of the requests (1).
19. The artificial intelligence model learns from the generated encoder transformation rules. 20 model weights containing parameter values obtained during the process and the model without changing the processing steps involved, the model's input data or apply the model to the output data produced by the electronic device (2) object position, face position, coordinate information obtained on it, The output data, including classification results and detection results, of the model 25 The output data obtained in the software environment in which it was created has the same meaning. restructured to enable it to be rearranged in a way that will carry any of the above requests characterized by the server (5) such a system (1). 19 20. With Automated Testing Suite, zero-value test inputs, model input and Sample test data relating to the multidimensional datasets used in their outputs, randomly generated test data, real image samples, and boundary cases ONNX is the software environment where the model is built using test inputs. The output data obtained from the intermediate model and the DLC model are repeated in 5 compare before applying the generated encoder transformation rules and object position, face position, coordinates obtained after application differences between the information, classification results and detection results to measure the extent to which the output data are consistent with each other, the amount of error, the model's output generation time, the level of matching of outputs, and the difference 10 characterized by the server (5) configured to calculate its limits a system like any of the above requests (1).
21. Multidimensional data used during the study for model optimization. the sizes of the arrays, the way input and output data are stored in memory, and much more. Memory alignment refers to the placement of three-dimensional data arrays within memory. organization, data type conversions, quantified and floating-point data representation their formats and whether the model runs on the CPU, GPU, or DSP. the processing time, memory usage and performance values that occur in this situation By analyzing the model, the electronic device (2) has low latency, low 20 To ensure it operates with low memory consumption and stable result production performance. from the above requests characterized by the server (5) configured for a system like any other (1).
22. The software environment in which the artificial intelligence model was created, the ONNX intermediate model, and 25 Compatibility between output data obtained on the DLC model. comparison, difference analysis, output matching, encoder to determine the level conversion rule creation, compliance testing, and performance measurement processes by evaluating the results related to the model on the electronic device (2) Compliance information for reliable and compatible operation 30 and to create reports, transfer the said data to the database (4) and to make it viewable by the user via the application (3) from the above requests characterized by the server (5) configured for a system like any other (1). 10 20 30