公开(公告)号：US11805025B1

公开(公告)日：2023-10-31

申请号：US17848048

申请日：2022-06-23

Applicant: ZHEJIANG LAB

Inventor： Hongsheng Wang ,  Shuibing He ,  Hujun Bao ,  Guang Chen

IPC: G06F8/41 ,  H04L41/14 ,  H04L45/44 ,  H04L41/16

CPC classification number: H04L41/145 ,  H04L41/16 ,  H04L45/44

Abstract: The present disclosure provides a neural network computing-oriented modeling method and apparatus for distributed data routing. The method includes the following steps: S1, designing the distributed attribute of a physical tensor: abstracting a mapping relationship between a logic tensor and the physical tensor into three distributed attributes including a broadcast attribute, a scatter attribute and a local reduction attribute; S2, deducing the distributed attribute of an output tensor: specifying the distributed attribute of an input tensor, and then deducing the legal distributed attribute of the output tensor according to the known distributed attribute of the input tensor; and S3, judging, according to the distributed attribute situation, whether an intermediate communication primitive needs to be inserted to obtain the distributed attribute of a local physical tensor. The difficulty of distributed design and development is low, and the development of application of a deep neural network large model is promoted.

公开(公告)号：US12272177B2

公开(公告)日：2025-04-08

申请号：US17950033

申请日：2022-09-21

Applicant: ZHEJIANG LAB

Inventor： Hongsheng Wang ,  Guang Chen ,  Hujun Bao

IPC: G06V40/20 ,  G06V10/56 ,  G06V10/82 ,  G06V20/64

Abstract: Disclosed are a method and apparatus for constructing a three-dimensional data set of a pedestrian re-identification based on a neural radiation field. The method includes the following steps: S1: capturing images of pedestrians to be entered by a group of cameras at different viewing angles; S2: generating a three-dimensional spatial position point set by sampling through camera rays in the scenario, and converting observation directions of the cameras corresponding to the three-dimensional spatial position point set into three-dimensional Cartesian unit vectors; and S3: inputting, into a multi-layer sensor, the three-dimensional spatial position point set and the observation directions converted into the three-dimensional Cartesian unit vectors, to output corresponding densities and colors. The method and apparatus of the present disclosure gives a brand-new method for constructing a pedestrian re-identification data set, and provides a new idea of data set construction.

公开(公告)号：US11782723B1

公开(公告)日：2023-10-10

申请号：US17992830

申请日：2022-11-22

Applicant: ZHEJIANG LAB

Inventor： Hongsheng Wang ,  Guang Chen ,  Lingfang Zeng ,  Aimin Pan

IPC: G06F8/40 ,  G06F9/38 ,  G06F8/41

CPC classification number: G06F9/3885 ,  G06F8/433 ,  G06F8/443

Abstract: Disclosed are an intermediate representation method and apparatus for parallel execution of graph computation. The method includes the following steps: S1: compiling a neural network into a computational graph on a computer; S2: defining branch states of tensor variables in the computational graph; S3: defining a data dependency relationship of the tensor variables in the computational graph; S4: defining a control dependency relationship of the tensor variables in the computational graph; S5: building a data dependency relationship graph of the tensor variables in the computational graph; S6: building a control dependency relationship graph of the tensor variables in the computational graph; and S7: transforming control dependencies into data dependencies. The present application derives, based on the dependency relationship, a parallel computing method that can execute the branch threads in parallel in the global computational graph, and optimizes the compilation efficiency of the computational graph.

公开(公告)号：US11615247B1

公开(公告)日：2023-03-28

申请号：US17830786

申请日：2022-06-02

Applicant: ZHEJIANG LAB

Inventor： Hongsheng Wang ,  Hujun Bao ,  Guang Chen ,  Chao Ma ,  Qing Liao

IPC: G06F40/30 ,  G06N3/08 ,  G06F40/295 ,  G06F40/242 ,  G06N3/047

Abstract: Disclosed are a labeling method and apparatus for named entity recognition of a legal instrument. The method includes steps: step S1: acquiring a legal text, and transforming the legal text into an index table; step S2: outputting a sentence feature encoding result; step S3: performing training and prediction; step S4: obtaining a set; step S5: obtaining a multi-head score transfer matrix; step S6: obtaining a score transfer matrix corresponding to the legal text; step S7: determining a recognized nested entity; and S8: constructing an entity labeling template by using the recognized nested entity. According to the present disclosure, a user tries to complete recognition of nested entity labeling by changing an input of the BERT model, and a multi-head selection matrix labeling thought of the present disclosure is used to relieve the difficulty in recognizing a long text and a nested entity in an NER task to a larger extent.

公开(公告)号：US11915135B2

公开(公告)日：2024-02-27

申请号：US17950028

申请日：2022-09-21

Applicant: ZHEJIANG LAB

Inventor： Hongsheng Wang ,  Guang Chen

IPC: G06N20/00 ,  G06N3/08 ,  G06F18/20

CPC classification number: G06N3/08 ,  G06F18/29 ,  G06N20/00

Abstract: The disclosure discloses a graph optimization method and apparatus for neural network computation. The graph optimization method includes the following steps: S1: converting a computation graph; S2: allocating a register; S3: defining a route selector for a redefined variable; S4: solving the route selector for the redefined variable; S5: defining a criterion of inserting the route selector for the redefined variable into a node; S6: analyzing a dominating edge set of the node for the redefined variable; S7: inserting the route selector for the redefined variable; and S8: renaming the redefined variable. The disclosure solves the problem of the corresponding route selection on a correct definition of the redefined variable when a node including the redefined variable in a computation graph in the compiling period flows through multiple paths of computation flow, reduces the memory cost and promotes the development of implementation application of a deep neural network model.

公开(公告)号：US11699290B1

公开(公告)日：2023-07-11

申请号：US17954129

申请日：2022-09-27

Applicant: ZHEJIANG LAB

Inventor： Hongsheng Wang ,  Guang Chen

IPC: G06V20/52 ,  G06V10/774 ,  G06V10/82 ,  G06V40/10 ,  G06T7/73 ,  G06T5/00 ,  G06T7/90 ,  G06V20/40

CPC classification number: G06V20/53 ,  G06T5/009 ,  G06T7/73 ,  G06T7/90 ,  G06V10/774 ,  G06V10/82 ,  G06V20/41 ,  G06V20/46 ,  G06V40/10 ,  G06T2207/10016 ,  G06T2207/10024 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30196 ,  G06T2207/30232

Abstract: Disclosed are a pedestrian re-identification method and apparatus based on local feature attention. The method includes the following steps: S1: obtaining an original surveillance video image data set, and dividing the original surveillance video image data set into a training set and a test set in proportion; and S2: performing image enhancement on the original surveillance video image training set to obtain enhanced images, and converting the enhanced images into sequence data. The pedestrian re-identification technology based on local feature attention uses a multi-head attention mechanism neural network to capture, extract video image feature sequences and replace convolution kernels in a convolutional neural network, uses fully connected layers and an activation function to combine local pedestrian feature sequences into complete pedestrian feature sequences through a weight matrix, performs prediction on the obtained pedestrian feature sequences, outputs position coordinates of pedestrians in the images and selects pedestrians to realize pedestrian re-identification.

公开(公告)号：US12039361B1

公开(公告)日：2024-07-16

申请号：US18494002

申请日：2023-10-25

Applicant: ZHEJIANG LAB

Inventor： Hongsheng Wang ,  Guang Chen ,  Fei Wu ,  Feng Lin

IPC: G06F9/48

CPC classification number: G06F9/48

Abstract: The present disclosure discloses a method for executing a task. The method includes: a master computing device node in a computing cluster system receives a task code of a to-be-executed task; the master computing device node divides the to-be-executed task into subtasks, and for each of the subtasks, the master computing device node determines operators required to execute the subtask based on the task code; the master computing device node respectively distributes the subtasks to computing nodes in the computing cluster system, such that for each of the computing nodes, the computing node generates an executable task subgraph for the computing node based on the operators required to execute the subtask distributed to the computing node and data transmission relationships between the operators required to execute the subtask distributed to the computing node, and runs the executable task subgraph to execute the to-be-executed task.

公开(公告)号：US11941514B2

公开(公告)日：2024-03-26

申请号：US17706734

申请日：2022-03-29

Applicant: ZHEJIANG LAB

Inventor： Hongsheng Wang ,  Hujun Bao ,  Guang Chen ,  Lingfang Zeng ,  Hongcai Cheng ,  Yong Li ,  Jian Zhu ,  Huanbo Zheng

IPC: G06N3/063 ,  G06N3/08

CPC classification number: G06N3/063 ,  G06N3/08

Abstract: The present disclosure discloses a method for execution of a computational graph in a neural network model and an apparatus thereof, including: creating task execution bodies on a native machine according to a physical computational graph compiled and generated by a deep learning framework, and designing a solution for allocating a plurality of idle memory blocks to each task execution body, so that the entire computational graph participates in deep learning training tasks of different batches of data in a pipelining and parallelizing manner.

公开(公告)号：US11941507B2

公开(公告)日：2024-03-26

申请号：US17954109

申请日：2022-09-27

Applicant: ZHEJIANG LAB

Inventor： Hongsheng Wang ,  Guang Chen

IPC: G06N3/04 ,  G06F18/20

CPC classification number: G06N3/04 ,  G06F18/29

Abstract: Disclosed are a data flow method and apparatus for neural network computation. The data flow method for neural network computation includes initializing the lifecycle of a variable in a computational graph; and defining a propagation rule for a variable in use to flow through a node. A definition of the variable is produced at a precursor node of the node, such that an input set of valid variables flowing through the node contains the variable. The method may be used on neural network computation in a deep learning training system.

公开(公告)号：US11861505B2

公开(公告)日：2024-01-02

申请号：US17833088

申请日：2022-06-06

Applicant: ZHEJIANG LAB

Inventor： Hongsheng Wang ,  Hujun Bao ,  Guang Chen

IPC: G06N3/10 ,  G06N7/01 ,  G06N3/04

CPC classification number: G06N3/10 ,  G06N3/04 ,  G06N7/01

Abstract: The disclosure discloses a method of executing dynamic graph for neural network computation and the apparatus thereof. The method of executing dynamic graph includes the following steps: S1: constructing and distributing an operator and a tensor; S2: deducing an operator executing process by an operator interpreter; S3: constructing an instruction of a virtual machine at runtime by the operator interpreter; S4: sending the instruction to the virtual machine at runtime by the operator interpreter; S5: scheduling the instruction by the virtual machine; and S6: releasing an executed instruction by the virtual machine. According to the method of executing dynamic graph for neural network computation and the apparatus thereof provided by the disclosure, runtime is abstracted to be the virtual machine, and the virtual machine acquires a sub-graph of each step constructed by a user in real time through the interpreter and schedules, the virtual machines issues, and executes each sub-graph.