公开(公告)号：US20240323120A1

公开(公告)日：2024-09-26

申请号：US18572948

申请日：2022-06-22

Applicant: SANECHIPS TECHNOLOGY CO., LTD.

Inventor： Siyu WANG ,  Fengsong LIU ,  Zhihua ZHU ,  Hengqi LIU

IPC: H04L45/7453

CPC classification number: H04L45/7453

Abstract: Embodiments of the present disclosure provide a packet matching method and apparatus, a storage medium, and an electronic device. The method includes: determining multiple Hash calculation results corresponding to a key value of a packet; indexing addresses in multiple counter groups respectively according to the multiple Hash calculation results, so as to determine multiple counters, wherein one counter is indexed in one counter group; determining one target counter from the multiple counters; acquiring a target entry at a corresponding position of an off-chip memory according to the address of the target counter; in a case where the target entry is equal to the key value, determining that the packet matches the target entry.

公开(公告)号：US20230353910A1

公开(公告)日：2023-11-02

申请号：US18002316

申请日：2021-04-28

Applicant: SANECHIPS TECHNOLOGY CO., LTD.

Inventor： Ru HUI ,  Hengqi LIU ,  Hong JIANG ,  Yi WANG

IPC: H04Q11/00

CPC classification number: H04Q11/0062 ,  H04Q11/0005 ,  H04Q2011/0033

Abstract: A data forwarding method and apparatus, a chip, and a non-transitory computer-readable storage medium are disclosed. The method may include: determining, according to a data switching request, an interface to be switched off and a target interface to be switched to (S110), where the interface to be switched off and the target interface to be switched to send same data and include a same number of data ports; and controlling, according to timestamp jumping points of the data ports included in the interface to be switched off and the target interface to be switched to, data forwarded to an optical transport network to be switched from the interface to be switched off to the target interface to be switched to (S120).

公开(公告)号：US20240394836A1

公开(公告)日：2024-11-28

申请号：US18694974

申请日：2022-03-16

Applicant: SANECHIPS TECHNOLOGY CO., LTD.

Inventor： Cong REN ,  Hengqi LIU ,  Ke XU ,  Dehui KONG ,  Jisong AI ,  Xin LIU ,  Jing YOU

IPC: G06T5/20

Abstract: The present disclosure provides a method for training an image enhancement model, the image enhancement model includes an enhancement module including convolution branches corresponding to brightness intervals; and the method includes: inputting a sample image to the image enhancement model, and acquire a result image output by the image enhancement model; calculating losses including an image loss of the result image relative to a Ground Truth image, and a first constraint loss of brightness histogram constraint of each of the convolution branches of an image output from each of the convolution branches relative to the Ground Truth image; adjusting the enhancement module according to the losses; and in a case where a training end condition is not met, returning to the operation of inputting the sample image to the image enhancement model. The present disclosure further provides an image enhancement method and a computer-readable medium.

公开(公告)号：US20230336643A1

公开(公告)日：2023-10-19

申请号：US18026864

申请日：2021-09-17

Applicant: SANECHIPS TECHNOLOGY CO., LTD

Inventor： Tonglei TAN ,  Feng ZHOU ,  Jinlin XU ,  Hengqi LIU

IPC: G06F9/22 ,  H04L69/22 ,  G06F9/30

CPC classification number: H04L69/22 ,  G06F9/226 ,  G06F9/30014 ,  G06F9/30043 ,  H04L2212/00

Abstract: The present disclosure provides a packet modification method and apparatus, a computer device, and a storage medium. The method includes: dividing a field to be modified that is related to packet encapsulation information into M containers; performing instruction extraction on a very long instruction word executing a modification command, to obtain N groups of initial instructions, where 2≤N≤M; processing the N groups of initial instructions to obtain N groups of source operands and N groups of modification field configuration information; determining, according to the N groups of modification field configuration information, N containers matched with the N groups of source operands, respectively; and modifying, according to the N groups of source operands, the N matched containers, respectively.

公开(公告)号：US20230305710A1

公开(公告)日：2023-09-28

申请号：US18018566

申请日：2021-07-05

Applicant: SANECHIPS TECHNOLOGY CO., LTD.

Inventor： Yijun ZHOU ,  Hengqi LIU ,  Jinlin XU ,  Feng ZHOU

IPC: G06F3/06

CPC classification number: G06F3/0613 ,  G06F3/0631 ,  G06F3/0673

Abstract: The present disclosure provides a writing method, including: writing writing-table data into a corresponding main storage module; performing a calculation on writing-table data in each target main storage module by using a first predetermined algorithm to obtain an auxiliary value, for any target main storage module, the first predetermined algorithm being used for performing a calculation on writing-table data stored in the target main storage module and corresponding writing-table data stored in at least one main storage module other than the target main storage module, an inverse operation of the first predetermined algorithm being used for performing a calculation on any auxiliary value to obtain writing-table data participating in the calculation of the auxiliary value; and storing the auxiliary value into a corresponding auxiliary storage module. The present disclosure further provides a reading method, a computer readable storage medium, a processor chip and an electronic device.

公开(公告)号：US20230177650A1

公开(公告)日：2023-06-08

申请号：US17924158

申请日：2021-05-06

Applicant: SANECHIPS TECHNOLOGY CO., LTD.

Inventor： Jing YOU ,  Hengqi LIU ,  Ke XU ,  Dehui KONG ,  Jisong AI ,  Xin LIU ,  Cong REN

IPC: G06T5/00 ,  G06T3/40

CPC classification number: G06T5/001 ,  G06T3/40 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: Disclosed are a method and apparatus for training an image restoration model, an electronic device, and a computer-readable storage medium. The method for training an image restoration model includes: pre-processing training images to obtain a low-illumination image sample set (110); determining, based on low-illumination image samples in the low-illumination image sample set and the image restoration model, a weight coefficient of the image restoration model (120), wherein the image restoration model is a neural network model determined on a U-Net network and a deep residual network; and adjusting the image restoration model according to the weight coefficient, and further training the adjusted image restoration model using the low-illumination image samples until the image restoration model restores parameters of all the low-illumination image samples in the low-illumination image sample set into a preset range (130).