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: 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 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: 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: A system may comprise a neural processing unit (NPU) including at least one memory and a plurality of processing elements (PEs) capable of performing operations for at least one artificial neural network (ANN) model. The plurality of PEs may include an adder, a multiplier, and an accumulator. The plurality of PEs may include a first group of PEs configured to operate on a first portion of a clock signal and a second group of PEs configured to operate on a second portion of the clock signal.

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 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: 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) mounted on a movable device for detecting object is provided. The NPU may comprise a plurality of processing elements (PEs), configured to process an operation of a first artificial neural network model (ANN) and an operation of a second ANN different from the first ANN; a memory configured to store a portion of a data of the first ANN and the second ANN; and a controller configured to control the PEs and the memory to selectively perform a convolution operation of the first ANN or the second ANN based on a determination data, wherein the determination data may include an object detection performance data of the first ANN and the second ANN, respectively.

Abstract: An artificial neural network memory system includes at least one processor configured to generate a data access request corresponding to an artificial neural network operation; and at least one artificial neural network memory controller configured to sequentially record the data access request to generate an artificial neural network data locality pattern of the artificial neural network operation and generate an advance data access request which predicts a next data access request of the data access request generated by the at least one processor based on the artificial neural network data locality pattern.

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: An electronic device mounted on a fixed or a movable apparatus is provided. The electronic device may comprise a neural processing unit (NPU), including a plurality of processing elements (PEs), configured to process an operation of an artificial neural network model trained to detect or track at least one object and output an inference result based on at least one image acquired from at least one camera; and a signal generator generating a signal applicable to the at least one camera.

Abstract: An electronic device mounted on a fixed or a movable apparatusapparatus is provided. The electronic device may comprise an image signal processor (ISP) for at least one camera; a neural processing unit (NPU), including a plurality of processing elements (PEs), configured to: process an operation of an artificial neural network model trained to detect or track at least one object, based on an input feature map generated from at least one image, which is acquired via the ISP from the at least one camera, and output an inference result; and a signal generator generating a signal applicable to the at least one camera or the ISP.

Abstract: A neural processing unit (NPU) for decoding video or feature map is provided. The NPU may comprise at least one processing element (PE) to perform an inference using an artificial neural network. The at least one PE may be configured to receive and decode data included in a bitstream. The data included in the bitstream may comprise data of a base layer. Alternatively, the data included in the bitstream may comprise data of the base layer and data of at least one enhancement layer. The data of the base layer included in the bitstream may include a first feature map. The data of the at least one enhancement layer included in the bitstream may include a second feature map.

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, the NPU may comprise at least one processing element (PE) for an artificial neural network, the at least one PE configured to receive and decode a bitstream. The bitstream may be received in a unit of data frame. One data frame of the bitstream may include a weight for an artificial neural network model, data of a base layer, and data of at least one enhancement layer. The data of the base layer included in the one data frame may include a first feature map, and the data of the at least one enhancement layer included in the one data frame may include a second feature map.

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: 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: 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.