公开(公告)号：US20240378660A1

公开(公告)日：2024-11-14

申请号：US18780496

申请日：2024-07-23

Applicant: DEEPX CO., LTD.

Inventor： Lok Won KIM

IPC: G06Q30/0601 ,  G06F3/0481 ,  G06F16/9538 ,  G06F18/214 ,  G06F18/40 ,  G06N3/04 ,  G06N3/08 ,  G06V10/764 ,  G06V10/82 ,  G06V10/94 ,  G06V20/20 ,  H04N23/63

Abstract: The present disclosure relates to a method for providing shopping information by using artificial neural network model and an electronic device performing the same. The method comprises: acquiring a product image using a camera module, displaying the product image on a display module, recognizing a product information including a trademark and a product information label by inputting the product image into an AI recognition model including an artificial neural network model, converting the product information into a query when the product information is recognized by the AI recognition model, transmitting the query to a server via a communication module, receiving a shopping information corresponding to the product information with respect to one or more shopping malls from the server via the communication module; and displaying the shopping information on the display module. The parameters of the artificial neural network are updated periodically by receiving the parameters via the server.

公开(公告)号：US20240321384A1

公开(公告)日：2024-09-26

申请号：US18678072

申请日：2024-05-30

Applicant: DEEPX CO., LTD.

Inventor： Lok Won KIM ,  Jeong Kyun YIM

IPC: G11C29/00 ,  G05B19/418 ,  G06F11/26 ,  G11C29/10

CPC classification number: G11C29/78 ,  G05B19/41875 ,  G06F11/26 ,  G11C29/10 ,  G05B2219/32368 ,  G05B2219/45031

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.

公开(公告)号：US20240211741A1

公开(公告)日：2024-06-27

申请号：US18594928

申请日：2024-03-04

Applicant: DEEPX CO., LTD.

Inventor： Seong Jin LEE ,  Jin Gun SONG ,  Lok Won KIM

IPC: G06N3/063

CPC classification number: G06N3/063

Abstract: A system-on-chip (SoC) may comprise a semiconductor substrate; a first circuitry, disposed on the semiconductor substrate, provided for a first neural processing unit (NPU) configured to perform operations of an artificial neural network (ANN) model; a second circuitry, disposed on the semiconductor substrate, provided for a second NPU configured to perform operations of an ANN model; and a third circuitry, disposed on the semiconductor substrate, configured to generate a control signal to selectively output one or more clock signals, wherein each of the first NPU and the second NPU may include a plurality of processing elements (PEs), and the plurality of PEs may include an adder, a multiplier, and an accumulator, and wherein the one or more clock signals may include an original clock signal and one or more phase-delayed clock signals based on a phase of the original clock signal.

公开(公告)号：US20230297519A1

公开(公告)日：2023-09-21

申请号：US17430323

申请日：2020-12-03

Applicant: DEEPX CO., LTD.

Inventor： Lok Won KIM

IPC: G06F13/16 ,  G06F13/18

CPC classification number: G06F13/1668 ,  G06F13/18

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.

公开(公告)号：US20230281982A1

公开(公告)日：2023-09-07

申请号：US18317728

申请日：2023-05-15

Applicant: DEEPX CO., LTD.

Inventor： Lok Won KIM ,  Ha Joon YU

IPC: G06V10/82 ,  G06V10/77

CPC classification number: G06V10/82 ,  G06V10/7715

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.

公开(公告)号：US20230276063A1

公开(公告)日：2023-08-31

申请号：US18312677

申请日：2023-05-05

Applicant: DEEPX CO., LTD.

Inventor： Lok Won KIM ,  Ha Joon YU

IPC: H04N19/436 ,  H04N19/172 ,  H04N19/30 ,  H04N19/136 ,  G06N3/08 ,  G06N3/045

CPC classification number: H04N19/436 ,  H04N19/172 ,  H04N19/30 ,  H04N19/136 ,  G06N3/08 ,  G06N3/045

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.

公开(公告)号：US20230232080A1

公开(公告)日：2023-07-20

申请号：US17311693

申请日：2020-12-07

Applicant: DEEPX CO., LTD.

Inventor： Lok Won KIM

IPC: H04N21/658 ,  H04N21/2343 ,  H04N21/466 ,  H04N21/4722 ,  H04N23/65 ,  G06T19/00

CPC classification number: H04N21/6581 ,  H04N21/234381 ,  H04N21/4666 ,  H04N21/4722 ,  H04N23/651 ,  G06T19/006

Abstract: A mobile artificial neural network device is provided. The mobile artificial neural network device includes a camera configured to output a video of a product at a first frame rate, an AI recognition model configured to recognize a product information by receiving the product video, an artificial neural network processor configured to drive the AI recognition model at a second frame rate, and a display module configured to display the video of the product at the first frame rate and display the product information at the second frame rate.

公开(公告)号：US20220206978A1

公开(公告)日：2022-06-30

申请号：US17499871

申请日：2021-10-13

Applicant: DEEPX CO., LTD.

Inventor： Lok Won KIM

IPC: G06F13/376 ,  G06F11/273 ,  G06N3/02

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.

公开(公告)号：US20220138586A1

公开(公告)日：2022-05-05

申请号：US17498752

申请日：2021-10-12

Applicant: DEEPX CO., LTD.

Inventor： Lok Won KIM

IPC: G06N3/10 ,  G06F12/0893 ,  G06F12/0879 ,  G06F12/02

Abstract: A memory system of an artificial neural network (ANN) includes a processor configured to process an ANN model; and an ANN memory controller configured to control a rearrangement of data of the ANN model stored in a memory and to operate the data of the ANN model stored in the memory in a read-burst mode based on ANN data locality information of the ANN model. The ANN memory controller may receive pre-generated ANN data locality information, or the processor may generate a plurality of data access requests sequentially so that the ANN memory controller may generate the ANN data locality information by monitoring the plurality of data access requests. The ANN memory controller prepares, based on an artificial neural network data locality, data before receiving a request from the processor in order to reduce a delay in the data supply of the memory to the processor.

公开(公告)号：US20220137869A1

公开(公告)日：2022-05-05

申请号：US17514028

申请日：2021-10-29

Applicant: DEEPX CO., LTD.

Inventor： Lok Won KIM

IPC: G06F3/06 ,  G06N3/063

Abstract: A system for an artificial neural network (ANN) includes a main memory including a dynamic memory cell electrically coupled to a bit line and a word line; and a memory controller configured to selectively omit a restore operation during a read operation of the dynamic memory cell. The main memory may be configured to selectively omit the restoration operation during the read operation of the dynamic memory cell by controlling a voltage applied to the word line. The memory controller may be further configured to determine whether to perform the restoration operation by determining whether data stored in the dynamic memory cell is reused. Thus, the system optimizes an ANN operation of the processor by utilizing the ANN data locality of the ANN model, which operates at a processor-memory level.