公开(公告)号：US20230334334A1

公开(公告)日：2023-10-19

申请号：US17833088

申请日：2022-06-06

Applicant: ZHEJIANG LAB

Inventor： Hongsheng WANG ,  Hujun BAO ,  Guang CHEN

IPC: G06N3/10 ,  G06N3/04 ,  G06N7/00

CPC classification number: G06N3/10 ,  G06N3/04 ,  G06N7/005

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.

公开(公告)号：US20230162048A1

公开(公告)日：2023-05-25

申请号：US17726563

申请日：2022-04-22

Applicant: ZHEJIANG LAB

Inventor： Hongsheng WANG ,  Wei HUA ,  Hujun BAO ,  Fei YANG

IPC: G06N3/10 ,  G06N3/04

CPC classification number: G06N3/10 ,  G06N3/04

Abstract: Disclosed is a method for adapting a deep learning framework to a hardware device based on a unified backend engine, which comprises the following steps: S1, adding the unified backend engine to the deep learning framework; S2, adding the unified backend engine to the hardware device; S3, converting a computational graph, wherein the computational graph compiled and generated by the deep learning framework is converted into an intermediate representation of the unified backend engine; S4, compiling the intermediate representation, wherein the unified backend engine compiles the intermediate representation on the hardware device to generate an executable object; S5, running the executable object, wherein the deep learning framework runs the executable object on the hardware device; S6: managing memory of the unified backend engine.

公开(公告)号：US20230410560A1

公开(公告)日：2023-12-21

申请号：US17950033

申请日：2022-09-21

Applicant: ZHEJIANG LAB

Inventor： Hongsheng WANG ,  Guang CHEN ,  Hujun BAO

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

CPC classification number: G06V40/25 ,  G06V20/64 ,  G06V10/56 ,  G06V10/82

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：US20230351212A1

公开(公告)日：2023-11-02

申请号：US17837233

申请日：2022-06-10

Applicant: ZHEJIANG LAB

Inventor： Hongsheng WANG ,  Qing LIAO ,  Hujun BAO ,  Guang CHEN

IPC: G06N5/02

CPC classification number: G06N5/022

Abstract: The disclosure provides a semi-supervised method and apparatus for public opinion text analysis. The semi-supervised method includes: first acquiring a public opinion data set, and preprocessing the data set; performing a data augmentation algorithm on preprocessed samples to generate data augmented samples; generating category labels for the unlabeled samples in the data set in an unsupervised extraction and clustering manner; calculating similarities of word vector latent semantic spaces and performing linear interpolation operation to generate, according to an operation result, similarity interpolation samples; constructing a final training sample set; adopting a semi-supervised method, inputting the final training sample set into a pre-trained language model to train the model to obtain a classification model; and predicting the test set by using the classification model to obtain a classification result.

公开(公告)号：US20230274129A1

公开(公告)日：2023-08-31

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：US20230177312A1

公开(公告)日：2023-06-08

申请号：US17739205

申请日：2022-05-09

Applicant: ZHEJIANG LAB

Inventor： Hongsheng WANG ,  Hujun BAO ,  Wei HUA ,  Weiqiang JIA

IPC: G06N3/04 ,  G06F9/54 ,  G06F9/48 ,  G06F8/36

CPC classification number: G06N3/0454 ,  G06F9/545 ,  G06F9/4881 ,  G06F8/36

Abstract: Disclosed is a method for distributed type training adaptation and apparatus in a deep learning framework and an AI accelerator card. The method includes the following steps: S1: the deep learning framework supports single-card configuration in a newly added AI accelerator card, and sub-steps thereof are as follows: S11: the deep learning framework supports new hardware; S12: the deep learning framework supports a device thread of the new hardware; S13: the deep learning framework supports a memory operation of the new hardware; and S14: the deep learning framework supports an operator kernel function of the new hardware; S2: the deep learning framework supports multi-card configuration in the newly added AI accelerator card; S3: the deep learning framework supports tensor segmentation and multi-card distribution; and S4: the deep learning framework supports multi-card collective communication in the newly added AI accelerator card.

公开(公告)号：US20230353458A1

公开(公告)日：2023-11-02

申请号：US17848048

申请日：2022-06-23

Applicant: ZHEJIANG LAB

Inventor： Hongsheng WANG ,  Shuibing HE ,  Hujun BAO ,  Guang CHEN

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

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：US20230351145A1

公开(公告)日：2023-11-02

申请号：US17838342

申请日：2022-06-13

Applicant: ZHEJIANG LAB

Inventor： Hongsheng WANG ,  Bowen TAN ,  Hujun BAO ,  Guang CHEN

IPC: G06N3/04 ,  G06F16/901 ,  G06F9/38

CPC classification number: G06N3/04 ,  G06F16/9024 ,  G06F9/3885

Abstract: The present disclosure provides a pipelining and parallelizing graph execution method for neural network model computation and apparatus, and provides a pipelining and parallelizing graph execution method for neural network model computation and apparatus in a deep learning training system. The method includes the graph execution flow in a neural network model computation process and a process of cooperative work of all functional modules. The pipelining and parallelizing graph execution method for neural network model computation includes creating a graph executive on a native machine according to a physical computation graph compiled and generated by a deep learning framework.

公开(公告)号：US20230259774A1

公开(公告)日：2023-08-17

申请号：US17714454

申请日：2022-04-06

Applicant: ZHEJIANG LAB

Inventor： Hongsheng WANG ,  Wei HUA ,  Weiqiang JIA ,  Hujun BAO

IPC: G06N3/08 ,  G06N3/04

CPC classification number: G06N3/082 ,  G06N3/04

Abstract: The disclosure discloses a method of neural network model computation-oriented intermediate representation and apparatus thereof. The method includes the following steps: S1, parsing an input model file so as to acquire topological structure information of a neural network; S2, constructing a logical computation graph; S21, inferring physical layout information of each operator in the logical computation graph; S22, inferring meta attributes of each operator in the logical computation graph; S23, inferring description information of input and output logical tensors of each operator in the logical computation graph; S3, constructing a physical computation graph; S31, generating a physical computation graph, etc.