Abstract: A method for providing a user interface (UI) for visualization of an artificial intelligence model, performed by a user terminal, is disclosed. The method may include: receiving an artificial intelligence model to be a target of visualization; rendering, on the user interface, a topology of a graph format that visually represents an abstracted structure of the artificial intelligence model, wherein the graph format includes a node representing an operation unit of the artificial intelligence model and an edge connecting nodes; displaying, on the user interface, a topology change option object having a correlation with a selected area when a user selection input selecting an area on the topology of the user interface is obtained; and re-rendering a topology in which a change is reflected, on the user interface, based on a user change input requesting the change on the topology change option object.

Abstract: In accordance with an embodiment of the present disclosure, a method for visualizing an artificial intelligence model, performed by a server, is disclosed. The method comprises: receiving an artificial intelligence model from a user terminal, generating an intermediate representation structure by abstracting the artificial intelligence model, converting the intermediate representation structure into structured text data for visualization of the artificial intelligence model using an attribute value included in the intermediate representation structure and transmitting the structured text data to the user terminal so that the user terminal renders a topology of a graph format for the artificial intelligence model.

Abstract: A method for providing a user interface (UI) for visualization of an artificial intelligence model, performed by a user terminal is disclosed. The method comprising: receiving a first artificial intelligence model and a second artificial intelligence model to be targets of visualization, generating a first topology of a graph format that visually represents an abstracted structure of the first artificial intelligence model and generating a second topology of a graph format that visually represents an abstracted structure of the second artificial intelligence model, wherein the graph format includes a node representing an operation unit of an artificial intelligence model and an edge connecting nodes, and rendering the first topology and the second topology to be visually comparable on the user interface by rendering the first topology on a first area of the user interface and rendering the second topology on a second area of the user interface.

Abstract: In one embodiment, a method for transforming an artificial model between a framework and an intermediate representation, performed by a computing device, is disclosed. The method may comprise: receiving target model operator information regarding target model operators executable in a target framework; determining a target module corresponding to each of the target model operators from the target model operator information, based on an execution-based analysis method using execution of a test artificial intelligence model, or an embedding-based analysis method using embedding transformation between a model operator and an atomic operator independent of a framework; and generating a target mapping table for the target framework by mapping each of the target model operators and the determined target module, and wherein the target module, as the intermediate representation of the target model operator, is composed of a combination of one or more nodes corresponding to the atomic operator.

Abstract: A method for converting a source artificial intelligence model to a target artificial intelligence model for a framework transformation is disclosed.

Abstract: A method comprises receiving an intermediate representation structure that represents a target artificial intelligence model, with a node corresponding to an atomic operator independent of a framework and a module dependent on a target framework of the target artificial intelligence model, wherein the intermediate representation structure has a hierarchical structure in which one or more nodes constitute the module, receiving target platform information for identifying a target platform on which the target artificial intelligence model is to be executed and input data to be used in a simulation process of the target artificial intelligence model and generating an expected inference simulation result for a case where the target artificial intelligence model with the input data is to be executed on the target platform, without executing the target artificial intelligence model on the target platform, by using the intermediate representation structure, the target platform information, and the input data.

Abstract: The method for modifying operators by tracking a channel axis of an artificial intelligence model may comprise: receiving a first artificial intelligence model executable in a first framework; tracking, based on a forward-direction operation flow from a first reference operator to a second reference operator included in the first artificial intelligence model, a first forward candidate channel axis for each of intermediate tensors corresponding to a first intermediate operator; tracking, based on a backward-direction operation flow from the second reference operator to the first reference operator, a first backward candidate channel axis for each of the intermediate tensors; determining, based on the first forward candidate channel axis and the first backward candidate channel axis, a first channel axis for each of the intermediate tensors; and modifying, based on the determined first channel axis, at least some operators including the first intermediate operator within the first artificial intelligence model

Abstract: A method for transforming an artificial intelligence model for performance optimization of the artificial intelligence model is disclosed.

Abstract: A method for transforming performance of an artificial intelligence model is disclosed. The method comprises: receiving an intermediate representation structure that represents a target artificial intelligence model which is an object of performance transformation, with a node corresponding to an atomic operator independent of a framework and a module dependent on a target framework of the target artificial intelligence model, wherein the intermediate representation structure has a hierarchical structure in which one or more nodes constitute the module, changing at least a part of the intermediate representation structure by applying a model optimization tool to the intermediate representation structure and outputting a target artificial intelligence model to which a performance transformation has been applied, by using the changed intermediate representation structure.

Abstract: Provided are a method and apparatus for compressing a neural network model by using device characteristics. The method includes: obtaining the neural network model that is executed by a device; adjusting a target number of output channels of a target layer included in the neural network model, based on an arithmetic intensity obtained from a roofline model and a latency characteristic of a staircase pattern of the device; and compressing the neural network model such that the number of output channels of the target layer is equal to the adjusted target number of output channels.

Abstract: Provided is a quantization method of an artificial neural network model including a plurality of layers. The quantization method includes identifying an outlier from among activation elements output from a first layer among the layers of the artificial neural network model, determining and regularizing a weight to be regularized among weights applied to the first layer based on relevance with the identified outlier, and quantizing the artificial neural network model after the quantization.

Abstract: The method for adjusting a distortion of a field of view of a camera in an intelligent transportation system comprises: receiving a target image, generating a first extraction result by extracting a predefined target object within the target image and a second extraction result by extracting the target object within a reference image, determining whether a distortion of the field of view of the target camera exists, using a first comparison result between the first extraction result and the second extraction result and a predefined threshold, determining a distortion type of the field of view of the target camera, using the first comparison result, when the distortion of the field of view of the target camera exists, and adjusting the distortion of the field of view of the target camera by using a different field of view adjustment scheme according to the distortion type of the target camera.

Abstract: Provided is an inference method using a vision-language model (VLM). The VLM is pretrained to sequentially generate inference results for consecutive inputs according to an input prompt, and the inference method includes caching information associated with the input prompt acquired during an operation for generating a first inference result for a first input among the consecutive inputs to the VLM, maintaining the cached information after the first inference result is generated; and generating a second inference result for a second input following the first input among the consecutive inputs to the VLM, based on the cached information.

Abstract: Disclosed is a method for providing an alarm based on a behavior of a driver in a driver monitoring system. The method includes receiving a first image corresponding to the driver within a vehicle, generating first model output information which indicates possibility that an anomaly which compromises safety of the driver exists in the first image, from the first image, by using an artificial intelligence model. The method includes generating a first anomaly primary prediction result indicating whether the anomaly exists in the first image by comparing the first model output information with a first threshold, generating a first anomaly secondary prediction result by performing a first voting using the first anomaly primary prediction result and determining an anomaly alarm corresponding to the first image by performing a second voting using the first anomaly secondary prediction result.

Abstract: Disclosed are a method and system for changing a structure of a deep learning model based on a change in resolution of input data. The method of changing a structure of a deep learning model may include generating, by the at least one processor, a plurality of input data having different resolution by performing various resolution changes on input data having given resolution, performing, by the at least one processor, inference on each of the plurality of generated input data through a deep learning model, checking, by the at least one processor, the size of a feature map output by each of layers included in the deep learning model while the inference is performed, and changing, by the at least one processor, the structure of at least one of the layers based on the checked size of the feature map.

Abstract: According to an embodiment of the present disclosure, a method for providing an artificial intelligence-based model, performed by a computing device, is disclosed. The method includes providing a candidate model list comprising a plurality of candidate models which are artificial intelligence-based models. The method includes determining a target model to be converted or benchmarked, based on a first user input on the candidate model list. The method includes providing a candidate node list comprising a plurality of candidate nodes. The method includes determining a target node to be converted or benchmarked, based on a second user input on the candidate node list. The method includes converting the target model into a model supportable by the target node, based on information of the determined target model and information preset on the target node.

Abstract: Disclosed is a method and system for readjusting granularity of search results according to complexity of a query for generative search. An inference method may include extracting a length-by-length phrase from a document retrieved for a query of a user; determining a ranking of the length-by-length phrase in consideration of complexity of the query; and configuring a prompt for input to a generative language model based on the determined ranking. Here, the ranking of the length-by-length phrase may be determined such that a relatively long phrase has a higher ranking according to an increase in the complexity of the query.

Abstract: A method of compressing a neural network model for object recognition according to an embodiment of the present application includes: receiving an original model for object recognition trained based on a first data set; receiving a second data set of an analysis target; calculating object size information on sizes of objects included in an image of the second data set; performing pruning on at least one layer included in the original model based on the calculated object size information; and generating a compressed neural network model from the original model based on the results of performing the pruning.

Abstract: The effective network compression using simulation-guided iterative pruning according to various embodiments, can be configured so that, by means of an electronic device, a first neural network is pruned on the basis of a threshold value, a second neural network is generated, a gradient for each weighted value of the second neural network is calculated, and a third neural is acquired by applying the gradient to the first neural network.

Abstract: Provided are a method and apparatus for generating a safety control signal of a road. The method includes inputting road state information for a first time point, including a safety control signal for the first time point and dynamic information for the first time point obtained from a video of a road, to a prediction model, inferring dangerous situation prediction information for a second time point after the first time point, by using the prediction model, and generating a safety control signal notifying a risk of accident on the road for the second time point, based on the inferred dangerous situation prediction information, wherein the prediction model is trained by using a loss function configured by dangerous situation prediction information inferred for a specific time point from road state information before the specific time point, and dangerous situation measurement information calculated from road state information for the specific time point.