Abstract: An off-road travel assistive device includes a receiver configured to receive travel data of a vehicle, and an estimator. The estimator includes a road surface severity estimator configured to estimate road surface severity data based on the travel data of the vehicle. The device also includes a stuck probability determiner configured to determine probability of a stuck state of the vehicle occurring using the travel data and the road surface severity data. The stuck probability determiner determines stuck probability using a meta-model.

Abstract: A coil component includes a body having one surface and the other surface, opposing each other, both lateral surfaces respectively connecting the one surface and the other surface and opposing each other, and both end surfaces respectively connecting the both lateral surfaces and opposing each other; a coil unit disposed in the body; a first external electrode and a second external electrode, respectively connected to the coil unit and disposed to be spaced apart from each other on the one surface of the body; and a first insulating layer covering the other surface of the body, the both lateral surfaces of the body, and the both end surfaces of the body.

Abstract: Proposed are a wafer inspection apparatus and a method of inspecting a wafer using the wafer inspection apparatus. The proposed wafer inspection apparatus includes a horizontal magnetic field generation unit arranged proximate to a lateral surface of a wafer and forming a magnetic field in such a manner that lines of magnetic force propagate in a horizontal direction, a vertical magnetic field generation unit arranged under the wafer and generating a magnetic field in such a manner that lines of magnetic force propagate in a direction vertical to the wafer, an image measurement unit arranged over the wafer and measuring an image of the wafer, and a wafer movement stage moving the wafer in a first direction and a second direction.

Abstract: A neural network system for predicting a polling time and a neural network model processing method using the neural network system are provided. The neural network system includes a first resource to generate a first calculation result obtained by performing at least one calculation operation corresponding to a first calculation processing graph and a task manager to calculate a first polling time taken for the first resource to perform the at least one calculation operation and to poll the first calculation result from the first resource based on the calculated first polling time.

Abstract: Operating methods of computing devices which include a central processing unit and a plurality of sub-processors configured to execute a machine learning model, and computer-readable media storing instructions. For example, an operating method may include dynamically assigning one of a blocking mode or a non-blocking mode to each task of a plurality of tasks of the machine learning model to establish a policy, and executing the machine learning model and the tasks thereof based on the policy.

Abstract: A method of processing data for a deep learning system driven by a plurality of heterogeneous resources is provided. The method includes, when a first task including at least one of a plurality of operations is to be performed, receiving first path information indicating a first computing path for the first task. The first computing path includes a sequence of operations included in the first task and a driving sequence of resources for performing the operations included in the first task. The method further includes setting data representation formats of the resources for performing the operations included in the first task based on data representation information and the first path information. The data representation information indicates an optimized data representation format for each of the plurality of heterogeneous resources.

Abstract: Provided are a system for managing a calculation processing graph of an artificial neural network and a method of managing a calculation processing graph by using the system. A system for managing a calculation processing graph of an artificial neural network run by a plurality of heterogeneous resources includes: a task manager configured to allocate the plurality of heterogeneous resources to a first subgraph and a second subgraph that are to be run, the first subgraph and the second subgraph being included in the calculation processing graph; a first compiler configured to compile the first subgraph to be executable on a first resource among the plurality of heterogeneous resources; and a second compiler configured to compile the second subgraph to be executable on a second resource among the plurality of heterogeneous resources, wherein the first subgraph and the second subgraph are respectively managed through separate calculation paths.

Abstract: A display device includes: a light source unit including a plurality of light sources; a cover bottom including a horizontal surface on which the plurality of light sources are seated, and a side surface bent along an edge of the horizontal surface; an optical unit on the light source unit; a display panel on the optical unit; a first module film being transparent, supporting a back surface of the optical unit, and including a first edge portion attached to and fixed to an outer side of the side surface of the cover bottom; and a second module film being transparent, attached to a back surface of the display panel, and including a second edge portion attached to and fixed to an outer side of the edge portion.

Abstract: A method of operating a neural network system includes parsing, by a processor, at least one item of information related to a neural network operation from an input neural network model; determining, by the processor, information of at least one dedicated hardware device; and generating, by the processor, a reshaped neural network model by changing information of the input neural network model according to a result of determining the information of the at least one dedicated hardware device such that the reshaped neural network model is tailored for execution by the dedicated hardware device.

Abstract: A neural network system for predicting a polling time and a neural network model processing method using the neural network system are provided. The neural network system includes a first resource to generate a first calculation result obtained by performing at least one calculation operation corresponding to a first calculation processing graph and a task manager to calculate a first polling time taken for the first resource to perform the at least one calculation operation and to poll the first calculation result from the first resource based on the calculated first polling time.

Abstract: A method of operating a neural network system includes merging, by a processor, a first operation group in a first neural network and a second operation group in a second neural network, including identical operations, as a shared operation group; selecting, by the processor, a first hardware to execute the shared operation group, from among a plurality of hardware; and executing the shared operation group by using the first hardware.

Abstract: A display device includes: a light source unit including a plurality of light sources; a cover bottom including a horizontal surface on which the plurality of light sources are seated, and a side surface bent along an edge of the horizontal surface; an optical unit on the light source unit; a display panel on the optical unit; a first module film being transparent, supporting a back surface of the optical unit, and including a first edge portion attached to and fixed to an outer side of the side surface of the cover bottom; and a second module film being transparent, attached to a back surface of the display panel, and including a second edge portion attached to and fixed to an outer side of the edge portion.

Abstract: A display device includes a display panel and a cover window including a density control area and being located corresponding to a transmission direction of a light emitted from the display panel, a refractive index of the density control area being gradually changed, wherein the refractive index of the density control area is gradually changed through voids.

Abstract: An artificial neural network system for managing a task to be performed by heterogeneous resources executing an artificial neural network, the artificial neural network system including a model analyzer that receives an artificial neural network model and outputs sub-graph information generated based on the artificial neural network model including at least one of sub-graph, a detector that outputs awareness information about the heterogeneous resources, and a task manager that outputs a first request signal for performing a task with respect to each layer of first resource of the heterogeneous resources based on the sub-graph information and the awareness information, and a second request signal for performing an task with respect to each depth of a second resource of the heterogeneous resources.

Abstract: A neural network system for predicting a polling time and a neural network model processing method using the neural network system are provided. The neural network system includes a first resource to generate a first calculation result obtained by performing at least one calculation operation corresponding to a first calculation processing graph and a task manager to calculate a first polling time taken for the first resource to perform the at least one calculation operation and to poll the first calculation result from the first resource based on the calculated first polling time.

Abstract: A method of controlling computing operations in a deep neural network (DNN) is provided. A network structure of the DNN including a plurality of layers is analyzed. A hyper parameter is set based on the network structure and real-time context information of a system configured to drive the DNN. The hyper parameter is used for performing an early-stop function. Depth-wise jobs are assigned to resources included in the system based on the hyper parameter to execute the depth-wise jobs. Each of the depth-wise jobs includes at least a part of the computing operations. When an early-stop event for a first layer among the layers is generated while the plurality of depth-wise jobs are executed, a subset computing operations included in at least one second layer are performed and a remainder of the computing operations are stopped. The at least one second layer is arranged prior to the first layer.

Abstract: A method of processing data for a deep learning system driven by a plurality of heterogeneous resources is provided. The method includes, when a first task including at least one of a plurality of operations is to be performed, receiving first path information indicating a first computing path for the first task. The first computing path includes a sequence of operations included in the first task and a driving sequence of resources for performing the operations included in the first task. The method further includes setting data representation formats of the resources for performing the operations included in the first task based on data representation information and the first path information. The data representation information indicates an optimized data representation format for each of the plurality of heterogeneous resources.

Abstract: Operating methods of computing devices which include a central processing unit and a plurality of sub-processors configured to execute a machine learning model, and computer-readable media storing instructions. For example, an operating method may include dynamically assigning one of a blocking mode or a non-blocking mode to each task of a plurality of tasks of the machine learning model to establish a policy, and executing the machine learning model and the tasks thereof based on the policy.

Abstract: A method of processing data for a deep learning system driven by a plurality of heterogeneous resources is provided. The method includes, when a first task including at least one of a plurality of operations is to be performed, receiving first path information indicating a first computing path for the first task. The first computing path includes a sequence of operations included in the first task and a driving sequence of resources for performing the operations included in the first task. The method further includes setting data representation formats of the resources for performing the operations included in the first task based on data representation information and the first path information. The data representation information indicates an optimized data representation format for each of the plurality of heterogeneous resources.

Abstract: A display device includes a display panel including a plurality of pixel regions and displaying a picture; and a light path controlling member located on a front surface of the display panel and transmitting the picture output from the display panel, wherein the light path controlling member includes a plurality of separation walls and a plurality of light path portions between the plurality of separation walls, and wherein a period of the light path portion is different from a period of the pixel region.