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 function approximation unit includes a programmable function approximation circuit configured to directly compute a piecewise function according to a pre-stored coefficient; one or more dedicated function circuits configured to process at least one specific function among a reciprocal, a reciprocal square root, and a negative exponential function through a fixed hard-wired operation pipeline; and control logic configured to selectively activate either the programmable function approximation circuit or the dedicated function circuit according to the type of the nonlinear function to be processed.

Abstract: An electronic device according to various examples is provided. The electronic device may include a decoder for decoding a compressed video; and a neural processing unit (NPU) for outputting a result frame comprising a first target data in response to searching for the first target data in one or more frames of a first compressed video while the decoder is decoding the first compressed video comprising the one or more frames.

Abstract: According to one example of the present disclosure, the neural processing unit may comprise a processing element array configured to perform operations of a neural network model and a post-processing unit configured to process data output from the processing element array. The post-processing unit includes a first computation circuit that extracts a subset of classes for each bounding box by comparing class scores of classes and a second computation circuit configured to extract one or more bounding boxes by comparing a class confidence score of each bounding box with a threshold confidence score.

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: According to one example of the present disclosure, a post-processing unit may be provided. The post-processing unit may be implemented in register transfer level (RTL) code and designed to interface with a neural processing unit (NPU) configured for object detection computations of a neural network model. The post-processing unit may include a processing unit configured to filter a plurality of bounding boxes transmitted from the NPU and output only those that satisfy a particular condition and one or more input registers configured to store data output from the processing unit.

Abstract: A neural processing unit (NPU) includes an internal memory storing information on combinations of a plurality of artificial neural network (ANN) models, the plurality of ANN models including first and second ANN models; a plurality of processing elements (PEs) to process first operations and second operations of the plurality of ANN models in sequence or in parallel, the plurality of PEs including first and second groups of PEs; and a scheduler to allocate to the first group of PEs a part of the first operations for the first ANN model and to allocate to the second group of PEs a part of the second operations for the second ANN model, based on an instruction related to information on an operation sequence of the plurality of ANN models or further based on ANN data locality information. The first and second operations may be performed in parallel or in a time division.

Abstract: A method for providing shopping information using an electronic device is disclosed. The method includes inputting a product image acquired via a camera module into an AI recognition model including an artificial neural network to obtain recognition results, and determining whether product information is recognized from the product image. When the AI recognition model fails to recognize the product information, a user input interface is displayed together with recognition failure information on a display module. User input data is received through the user input interface, and the product information is determined based on the received user input data. A query based on the product information is transmitted to a server via a communication module, and shopping information corresponding to the transmitted query is received from the server and displayed on the display module.

Abstract: A neural processing unit includes a controller to receive a compiled machine code of an artificial neural network (ANN) model for predicting cardiovascular disease, the ANN model configured to receive sensing data of ECG, respiration, pulse rate, acceleration, and/or body temperature and to output a probability of disease onset for cerebral infarction, heart failure, and/or ischemic heart disease in a user; an input circuit configured to receive a plurality of input signals corresponding to the ANN model; a processing element (PE) array to perform a calculation of the ANN model; a special function unit (SFU) to perform a special function of calculating the ANN model; and an on-chip memory to store operation data of the ANN model, wherein the controller controls the PE array, the SFU, and the on-chip memory to process the ANN model according to data locality information of the ANN model included in the compiled machine code.

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 scheduler 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 neural processing unit (NPU), a method for driving an artificial neural network (ANN) model, and an ANN driving apparatus are provided. The NPU includes a semiconductor circuit that includes at least one processing element (PE) configured to process an operation of an artificial neural network (ANN) model; and at least one memory configurable to store a first kernel and a first kernel filter. The NPU is configured to generate a first modulation kernel based on the first kernel and the first kernel filter and to generate second modulation kernel based on the first kernel and a second kernel filter generated by applying a mathematical function to the first kernel filter. Power consumption and memory read time are both reduced by decreasing the data size of a kernel read from a separate memory to an artificial neural network processor and/or by decreasing the number of memory read requests.

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: According to an example of the present disclosure, a system is provided. The system may include: a plurality of heterogeneous processors configured to perform inference using a neural network model comprising a plurality of layers; a memory for storing the neural network model, wherein each layer of the neural network model comprises a plurality of parameters having different bit-widths; and a controller for assigning an operation for an arbitrary layer of the neural network model to an arbitrary processor among the plurality of heterogeneous processors. An operation for a first portion of the arbitrary layer of the neural network model may be assigned to a first processor among the plurality of heterogeneous processors. An operation for a second portion of the arbitrary layer of the neural network model may be assigned to a second processor among the plurality of heterogeneous processors.

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: An artificial neural network (ANN) system for generating a fused image is disclosed. The system includes a first sensor configured to acquire a first image having a first resolution and a first image characteristic, and a second sensor configured to acquire a second image having a second resolution different from the first resolution and a second image characteristic different from the first image characteristic. A processing unit executes a first artificial neural network model to generate a third image by processing the first and second images, and provides the third image as an input to a second artificial neural network model. The second artificial neural network model is trained to perform an inference task on the third image, the inference task being one of object classification, object detection, object segmentation, object tracking, event recognition, event prediction, anomaly detection, density estimation, event search, or measurement.

Abstract: An image processing method includes receiving an image including an object; classifying at least one object in the image using a first model on the basis of an artificial neural network configured to classify the at least one object by inputting the image; and obtaining an image having improved quality according to the at least one object by inputting the image in which the at least one object is classified by using at least one model among a plurality of second models on the basis of an artificial neural network configured to output a specialized processing applied image according to a particular object by inputting the received image.

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 scalable AI system includes a plurality of slots on a main board, at least one AI module mounted in the slots to perform AI operations, and a main processor that controls the entire system. A user can flexibly scale the overall AI computation performance of the system by adjusting the number of AI modules as needed. Each AI module is equipped with a low-power NPU specialized for AI operations and adopts standard form factors (e.g., M.2, E1.S) to improve economy and scalability. This can solve the problems of high-power consumption and cost, which are limitations of GPU-based systems, and can efficiently support various AI applications.

Abstract: A method may comprise receiving a first neural network (NN) model including one or more functions; generating a second NN model in a form of directed acyclic graph (DAG) including one or more graph modules by converting the one or more functions; calculating one or more scale values by obtaining maximum and minimum values of parameters input to the one or more graph modules; updating the parameters based on the one or more scale values; and generating a third NN model, in a form of machine code executable on a particular neural processing unit, including the updated parameters.

Abstract: According to an example of the present disclosure, a system is provided. A system may include a processor configured to output a memory control signal including an artificial neural network data locality, and a memory controller configured to receive the memory control signal from the processor and control a main memory in which data of an artificial neural network model corresponding to the artificial neural network data locality, is stored.