Abstract: A method for programming an activation function is provided. The method includes generating segment data for segmenting the activation function; segmenting the activation function into a plurality of segments using the segment data; and approximating at least one segment of the plurality of segments as a programmable segment. An apparatus for performing the method may include a programmable activation function generator configured to generate segment data for segmenting an activation function; segment the activation function into a plurality of segments using the generated segment data; and approximate at least one segment of the plurality of segments as a programmable segment. By using segment data, various non-linear activation functions, particularly newly proposed or known activation functions with some modifications, can be programmed to be processable in hardware.

Abstract: A neural processing unit (NPU) and a method of operating the same are provided. The NPU may include an artificial intelligence (AI) calculation unit configured to process artificial neural network calculation of at least one artificial neural network model; and an internal memory including at least one memory unit configured to store data of at least one domain among first to third domain data of the at least one artificial neural network model. The at least one memory unit may include a plurality of sub-memory units configured to perform time-division operation. A bandwidth of the at least one memory unit is based on a number of the plurality of sub-memory units.

Abstract: A neural processing unit includes an internal memory including a plurality of memory units; a controller configured to control read and write operations of data in at least one of an input feature map domain, a weight domain, and an output feature map domain with respect to each of the plurality of memory units based on an operation schedule in a machine code in which a plurality of operation steps of an artificial neural network model are set.

Abstract: A neural processing unit (NPU) for decoding video or feature map is provided. The NPU may include at least one processing element (PE) for an artificial neural network, the at least one PE configured to receive and decode data included in a bitstream. The data included in the bitstream may include data of a base layer; or the data of the base layer and data of at least one enhancement layer. An NPU for encoding video or feature map is also provided. The encoder NPU may include at least one PE for an artificial neural network, the at least one PE configured to receive and encode a transmitted video or feature map, wherein the at least one PE may be further configured to output a bitstream including data of a base layer and data of at least one enhancement layer.

Abstract: A neural processing unit (NPU) is capable of testing a component of the NPU in a running system, i.e., during runtime. The NPU includes a plurality of functional components, each of which includes an electronic circuit; at least one wrapper connected to at least one of the functional components; and an in-system component tester (ICT). The ICT performs a selection of one of the at least one functional component, in an idle state, as a component under test (CUT) and performs a test, via the at least one wrapper, of the selected functional component. The ICT may monitor states of the plurality of the functional components via the at least one wrapper, stop the test based on a detection of a collision due to an access to the selected functional component, and return a connection of the selected functional component to the at least one wrapper according to the stop.

Abstract: A method for stabilizing an image based on artificial intelligence includes acquiring tremor detection data with respect to the image, the tremor detection data acquired from two or more sensors; outputting stabilization data for compensating for an image shaking, the stabilization data outputted using an artificial neural network (ANN) model trained to output the stabilization data based on the tremor detection data; and compensating for the image shaking using the stabilization data. A camera module includes a lens; an image sensor to output an image captured through the lens; two or more sensors to output tremor detection data with respect to the image; a controller to output stabilization data based on the tremor detection data using an ANN model; and a stabilization unit to compensate for an image shaking using the stabilization data. The ANN model is trained to output the stabilization data based on the tremor detection data.

Abstract: Disclosed is a data cache or data management device for caching data between at least one processor and at least one memory, and supporting an artificial neural network (ANN) operation executed by the at least one processor. The data cache device or the data management device can comprise an internal controller for predicting the next data operation request on the basis of ANN data locality of the ANN operation. The internal controller monitors data operation requests associated with the ANN operation from among data operation requests actually made between the at least one processor and the at least one memory, thereby extracting the ANN data locality of the ANN operation.

Abstract: A method for stabilizing an image based on artificial intelligence includes acquiring tremor detection data with respect to the image, the tremor detection data acquired from two or more sensors; outputting stabilization data for compensating for an image shaking, the stabilization data outputted using an artificial neural network (ANN) model trained to output the stabilization data based on the tremor detection data; and compensating for the image shaking using the stabilization data. A camera module includes a lens; an image sensor to output an image captured through the lens; two or more sensors to output tremor detection data with respect to the image; a controller to output stabilization data based on the tremor detection data using an ANN model; and a stabilization unit to compensate for an image shaking using the stabilization data. The ANN model is trained to output the stabilization data based on the tremor detection data.

Abstract: A neural processing unit (NPU) for decoding video and/or feature map may include at least one processing element (PE) for an artificial neural network (ANN), the at least one PE to receive and decode a bitstream. The bitstream is received in units of frames, and one frame includes a weight for an ANN model, data of a base layer, and data of a plurality of enhancement layers. An NPU for encoding video and/or feature map may include at least one processing element (PE) for an artificial neural network (ANN), the at least one PE to encode an input video or feature map and to transmit the encoded input video or feature map as a bitstream. The at least one PE transmits the bitstream in units of frames, and one frame includes a weight for an ANN model, data of a base layer, and data of a plurality of enhancement layers.

Abstract: A neural processing unit (NPU) includes a controller including a scheduler, the controller configured to receive from a compiler a machine code of an artificial neural network (ANN) including a fusion ANN, the machine code including data locality information of the fusion ANN, and receive heterogeneous sensor data from a plurality of sensors corresponding to the fusion ANN; at least one processing element configured to perform fusion operations of the fusion ANN including a convolution operation and at least one special function operation; a special function unit (SFU) configured to perform a special function operation of the fusion ANN; and an on-chip memory configured to store operation data of the fusion ANN, wherein the schedular is configured to control the at least one processing element and the on-chip memory such that all operations of the fusion ANN are processed in a predetermined sequence according to the data locality information.

Abstract: An apparatus for recognizing an object in an image includes a preprocessing module configured to receive an image including an object and to output a preprocessed image by performing image enhancement processing on the received image to improve a recognition rate of the object included in the received image; and an object recognition module configured to recognize the object included in the image by inputting the preprocessed image to an input layer of an artificial neural network for object recognition.

Abstract: A computer-implemented apparatus installed and executed in a computer to search an optimal design of a neural processing unit (NPU), a hardware accelerator used for driving a computer-implemented artificial neural network (ANN) is disclosed. The NPU comprises a plurality of blocks connected in a form of pipeline, and the number of the plurality blocks and the number of the layers within each block of the plurality blocks are in need of optimization to reduce hardware resources demand and electricity power consumption of the ANN while maintaining the inference accuracy of the ANN at an acceptable level. The computer-implemented apparatus searches for and then outputs an optimal L value and an optimal C value when a first set of candidate values for a number of layers L and a second set of candidate values for a number of channels C per each layer of the ANN is provided.

Abstract: A system on chip (SoC) for testing a component in a system during runtime includes a plurality of functional components; a system bus for allowing the plurality of functional components to communicate with each other; one or more wrappers, each connected to one of the plurality of functional components; and an in-system component tester (ICT). The ICT monitors, via the wrappers, states of the functional components; selects, as a component under test (CUT), at least one functional component in an idle state; tests, via the wrappers, the selected at least one functional component; interrupts the testing step with respect to the selected at least one functional component, based on a detection of a collision with an access from the system bus to the selected at least one functional component; and allows a connection of the at least one functional component to the system bus, based on the interrupting step.

Abstract: A method for programming an activation function is provided. The method includes generating segment data for segmenting the activation function; segmenting the activation function into a plurality of segments using the segment data; and approximating at least one segment of the plurality of segments as a programmable segment. An apparatus for performing the method may include a programmable activation function generator configured to generate segment data for segmenting an activation function; segment the activation function into a plurality of segments using the generated segment data; and approximate at least one segment of the plurality of segments as a programmable segment. By using segment data, various non-linear activation functions, particularly newly proposed or known activation functions with some modifications, can be programmed to be processable in hardware.

Abstract: A neural processing unit (NPU) for testing a component during runtime is provided. The NPU may include a plurality of functional components including a first functional component and a second functional component. At least one of the plurality of functional components may be driven for calculation of an artificial neural network. Another one of the plurality of functional components may be selected as a component under test (CUT). A scan test may be performed on the at least one functional component selected as the CUT. A tester for detecting a defect of an NPU is also provided. The tester may include a component tester configured to communicate with at least one functional component of the NPU, select the at least one functional component as a CUT, and perform a scan test for the selected CUT.

Abstract: An apparatus for recognizing an object in an image includes a preprocessing module configured to receive an image including an object and to output a preprocessed image by performing image enhancement processing on the received image to improve a recognition rate of the object included in the received image; and an object recognition module configured to recognize the object included in the image by inputting the preprocessed image to an input layer of an artificial neural network for object recognition.

Abstract: A neural processing unit includes a mode selector configured to select a first mode or a second mode; and processing element (PE) array operating in one of the first mode and the second mode and including a plurality of processing elements arranged in PE rows and PE columns, the PE array configured to receive an input of first input data and an input of second input data, respectively. In the second mode, the first input data is inputted in a PE column direction of the PE array and is transmitted along the PE column direction while being delayed by a specific number of clock cycles, and the second input data is broadcast to the plurality of processing elements of the PE array to which the first input data is delayed by the specific number of clock cycles.

Abstract: A control method of an image signal processor for an artificial neural network may be configured to include a step of acquiring an image, a step of determining at least one image characteristic data corresponding to the image, and a step of determining an image correction parameter (SFR preset) for improving an inference accuracy of an artificial neural network model based on the at least one of image characteristic data and an inference accuracy profile of an artificial neural network model.

Abstract: A method for stabilizing an image based on artificial intelligence includes acquiring tremor detection data with respect to the image, the tremor detection data acquired from two or more sensors; outputting stabilization data for compensating for an image shaking, the stabilization data outputted using an artificial neural network (ANN) model trained to output the stabilization data based on the tremor detection data; and compensating for the image shaking using the stabilization data. A camera module includes a lens; an image sensor to output an image captured through the lens; two or more sensors to output tremor detection data with respect to the image; a controller to output stabilization data based on the tremor detection data using an ANN model; and a stabilization unit to compensate for an image shaking using the stabilization data. The ANN model is trained to output the stabilization data based on the tremor detection data.

Abstract: A neural processing unit includes a mode selector configured to select a first mode or a second mode; and processing element (PE) array operating in one of the first mode and the second mode and including a plurality of processing elements arranged in PE rows and PE columns, the PE array configured to receive an input of first input data and an input of second input data, respectively. In the second mode, the first input data is inputted in a PE column direction of the PE array and is transmitted along the PE column direction while being delayed by a specific number of clock cycles, and the second input data is broadcast to the plurality of processing elements of the PE array to which the first input data is delayed by the specific number of clock cycles.