Abstract: A method for controlling a data allocatable neural processing unit is provided. The method may comprise: determining a dependency determination between data blocks of a specific layer of at least one neural network model; allocating memory banks to store the data blocks based on the dependency determination; and storing each data block in the allocated memory bank.

Abstract: An image processing method performed by a neural processing unit is disclosed. The method includes receiving an input image including at least one object and processing the input image using a first model via the neural processing unit to detect a particular object among the at least one object, the first model being an artificial neural network-based object detector trained to detect the particular object in an image. The method further includes processing the input image using a second model via the neural processing unit to blur the particular object in the input image, the second model being an artificial neural network trained to blur a region corresponding to the particular object. An output image including the blurred particular object is generated.

Abstract: Methods and devices of a function approximation unit configured to approximate a nonlinear function within a neural processing unit are described. According to one embodiment, the method includes storing an input value through an input register of the function approximation unit, transmitting the input value to a selected one of a plurality of preprocessing circuits of the unit according to a control signal, generating a preprocessing result corresponding to the input value by the selected one of the preprocessing circuits, transmitting the preprocessing result to a programmable function approximation circuit and a selected one of a plurality of post-processing circuits of the unit, generating an approximated function output based on the preprocessing result in the programmable function approximation circuit, and generating a final output value by post-processing the preprocessing result or the approximated function output in the selected one of the post-processing circuits.

Abstract: A neural processing apparatus is disclosed. The apparatus includes a memory array including a plurality of physically distinct memory banks, configurable interconnect circuitry coupled to the plurality of memory banks, and a controller. The controller is configured to dynamically reallocate the plurality of memory banks into variable-sized memory regions corresponding to different data types required for an inference operation of a neural network model. The controller adjusts a number of memory banks allocated to each data type for a current layer of the neural network model based on configuration information derived from a structure of the neural network model. The controller is further configured to enable data access between the reallocated memory banks and a processing engine via the configurable interconnect circuitry.

Abstract: A neural processing unit for performing inference operations of a large-scale language model based on an artificial neural network is disclosed.

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 mobile electronic device may include a main memory for storing weight values of a compiled and quantized large language model, a processing core for performing an inference operation based on an attention mechanism on text based on the large language model, a controller for controlling the operation of the processing core and the main memory and for scheduling the inference operation, and a special function unit for performing vector operations and scalar operations necessary for the inference operation. Quantization and dequantization are performed in the inference operation of a large language model, enabling on-device inference operations with minimal performance degradation.

Abstract: Provided is a method for performing an aging test on a neural processing unit (NPU) with a capability of a runtime test. The method may comprise: performing an aging test on the NPU which comprises a plurality of functional components. The plurality of functional components may comprise at least one memory and plural processing elements. The performing of the aging test may include: performing a scan test on the NPU to verify whether at least one functional component in the NPU is defective or not; and performing a memory test on the at least one memory. At least one of the scan test and the memory test may be repeatedly performed to put a stress on the NPU for the aging test. The aging test may be repeated by a predetermined number.

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: An image processing method is disclosed. The method includes receiving an input image including at least one object, and classifying the at least one object in the input image using a first model based on an artificial neural network trained to classify objects into one of a plurality of predetermined categories. At least one second model corresponding to the classified category of the at least one object is determined from among a plurality of second models, each of which is based on an artificial neural network trained to output a specialized processing applied image specific to a respective category. An output image is obtained by inputting the input image, or a region thereof corresponding to the at least one object, into the determined at least one second model.

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.