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 coil component is provided. The coil component includes a body having fifth and sixth surfaces opposing each other, first and second surfaces respectively connecting the fifth and sixth surfaces of the body and opposing each other, and third and fourth surfaces respectively connecting the first and second surfaces of the body and opposing each other in one direction, a recess disposed in an edge between one of the first and second surfaces of the body and the sixth surface of the body, a coil portion disposed inside the body and exposed through the recess, and an external electrode including a connection portion disposed in the recess and connected to the coil portion, and a pad portion disposed on one surface of the body. A length of the pad portion in the one direction is greater than a length of the connection portion in the one direction.

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.

Abstract: Provided are a neural network system, an application processor including the same, and a method of operating the neural network system. The neural network system includes an operation allocating manager configured to divide a request including a plurality of operations for neural network processing into a plurality of sub-requests each including at least one operation, allocate the plurality of sub-requests to dedicated hardware and one or more pieces of general-purpose hardware, and allocate memories for input and output for each of the plurality of sub-requests based on cost information indicating respective costs needed for hardware to access the memories; and a neural network device configured to execute the sub-requests by using the dedicated hardware and the one or more pieces of general-purpose hardware according to a result of the operation allocation. The operation allocating manager allocates different memories to at least two sub-requests of the plurality of sub-requests.

Abstract: Disclosed is an operating method of a semiconductor device, including acquiring resource information on a plurality of hardware resources, receiving a compression request or a decompression request for data, acquiring context information on the semiconductor device, in response to receiving the compression request or the decompression request for the data, selecting a compression algorithm for compressing or decompressing the data, based on the context information, selecting, among the plurality of hardware resources, a hardware resource for performing the selected compression algorithm, based on the acquired resource information, and compressing or decompressing the data using the selected compression algorithm and the selected hardware resource.

Abstract: Provided are a mobile device and an operation method of the mobile device. The operation method controlled by a central processing unit of the mobile device includes, in response to an initialization request with respect to a memory device of the mobile device, setting a first type area of the memory device, which is not initialized as a first value, and processing an operation command with respect to the first type area.

Abstract: A neural network computing system includes a processor, and a deep learning framework under control of the processor. The framework obtains model information of a neural network model by reading at least one neural network model file, creates a neural network graph of the neural network model using the model information, adjusts the neural network graph such that the neural network model corresponds to an operation of a first hardware computing device and an operation of a second hardware computing device, divides the neural network model into a plurality of sub-models, including first and second sub-models, pipelines the first and second hardware computing devices by allocating the first and second sub-models to the first and second hardware computing devices, respectively, and detects a reduced hardware latency measurement from among a plurality of hardware latency measurements obtained by changing at least one of hardware latencies of the first and second sub-models.