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：US20240061663A1

公开(公告)日：2024-02-22

申请号：US18495707

申请日：2023-10-26

Applicant: ZHEJIANG LAB

Inventor： Lei XUE ,  Tao ZOU ,  Ruyun ZHANG ,  Jun ZHU

IPC: G06F8/41 ,  G06F9/445

CPC classification number: G06F8/443 ,  G06F9/44505

Abstract: The present disclosure discloses a compiling system for a compiling system and a compiling method for a programmable network element. Aiming at the diversified requirements of network modals for the underlying hardware resources, the system realizes the integration and fusion mechanism of computing/storage/forwarding/security, and abstracts network element equipment including heterogeneous hardware resources and isomeric hardware resources into a logical network element irrelevant to the underlying hardware; performs advanced abstract encapsulation on the heterogeneous hardware resources and isomeric hardware resources, supports flexible calling of underlying hardware and software resources, uses the technology of functional equivalent replacement between heterogeneous hardware resources and isomeric hardware resources, realizes switching and co-processing of network modals among hardware resources according to actual requirements, allocates heterogeneous hardware resources according to modal characteristics, and calls various compilers to automatically generate and optimize modal packet processing pipelines.

公开(公告)号：US20240054319A1

公开(公告)日：2024-02-15

申请号：US17954109

申请日：2022-09-27

Applicant: ZHEJIANG LAB

Inventor： Hongsheng WANG ,  Guang CHEN

IPC: G06N3/04 ,  G06K9/62

CPC classification number: G06N3/04 ,  G06K9/6296

Abstract: Disclosed are a data flow method and apparatus for neural network computation. The method includes: step 1, initializing the lifecycle of a variable in a computational graph, i.e., initializing a time period from the start of a definition of the variable to the end of use as the lifecycle of the variable in the computational graph; and step 2, defining a propagation rule for a variable in use to flow through a node, i.e., defining that in the case that a variable at a certain node in the computational graph is used, 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 application discloses a data flow modeling method and apparatus for neural network computation in a deep learning training system.

公开(公告)号：US20240046122A1

公开(公告)日：2024-02-08

申请号：US18491817

申请日：2023-10-23

Applicant: ZHEJIANG LAB

Inventor： Feng LIN ,  Yunhe PAN

IPC: G06N5/022 ,  G06V10/77 ,  G06V10/80 ,  G06F40/30

CPC classification number: G06N5/022 ,  G06V10/7715 ,  G06V10/806 ,  G06F40/30 ,  G06V40/25

Abstract: A method and an apparatus for cross-media corresponding knowledge generation. The method comprises: generating a second knowledge unit of a second medium according to a first knowledge unit of a predefined first medium; generating a first feature parameter vector corresponding to the first knowledge unit and a second feature parameter vector corresponding to the second knowledge unit; mapping the first feature parameter vector and the second feature parameter vector to a corresponding two-dimensional spherical feature surface to obtain a first feature point of the first feature parameter vector on the corresponding two-dimensional spherical feature surface and a second feature point of the second feature parameter vector on the corresponding two-dimensional spherical feature surface; indexing the first feature point and the second feature point to obtain a first index and a second index; and generating a bidirectional index corresponding relationship between the first knowledge unit and the second knowledge unit.

公开(公告)号：US20240038325A1

公开(公告)日：2024-02-01

申请号：US18122251

申请日：2023-03-16

Applicant: ZHEJIANG LAB

Inventor： Shang YU ,  Zhipeng ZHONG ,  Liang XU ,  Jianshun TANG ,  Yitao WANG

IPC: G16B15/30 ,  G16C10/00 ,  G06N10/20

CPC classification number: G16B15/30 ,  G16C10/00 ,  G06N10/20

Abstract: The application discloses a reverse virtual screening platform and method based on programmable quantum computing, the method includes the following steps: S1, for a given micromolecule and a target protein molecule, calculating a binding interaction graph of the given micromolecule and the target protein molecule on a computer according to different distances between pharmacophores; S2, encoding, according to an adjacency matrix of the binding interaction graph, the binding interaction graph into a quantum reverse virtual screening platform by decomposing the adjacency matrix; and S3, performing Gaussian boson sampling by the quantum reverse virtual screening platform. The reverse virtual screening platform and method based on programmable quantum computing provided by the present application are implemented by an optical quantum computer system based on a time domain.

公开(公告)号：US20240036860A1

公开(公告)日：2024-02-01

申请号：US18360840

申请日：2023-07-28

Applicant: ZHEJIANG LAB

Inventor： Jingsong LI ,  Hongyi NI ,  Tianshu ZHOU ,  Yu TIAN

IPC: G06F8/71 ,  G06F8/65

CPC classification number: G06F8/71 ,  G06F8/65

Abstract: The present disclosure discloses a method and system for automatically and quickly deploying a front-end processor based on gray release. The system includes a user management module, a front-end processor engineering configuration module, a version iteration module and an engineering code version management repository, where the version iteration module is connected with the engineering code version management repository, the user management module and the front-end processor engineering configuration module, a code is obtained through the engineering code version management repository to perform updating or rollback of a current code, an operating permission of the front-end processor is obtained by using the user management module, an engineering configuration parameter is obtained from the front-end processor engineering configuration module for engineering gray release of a plurality of front-end processors, and a task scheduling function therein is called.

公开(公告)号：US20240021312A1

公开(公告)日：2024-01-18

申请号：US18352216

申请日：2023-07-13

Applicant: ZHEJIANG LAB

Inventor： Jingsong LI ,  Feng WANG ,  Shengqiang CHI ,  Yu TIAN ,  Tianshu ZHOU

IPC: G16H50/20 ,  G16H50/30

CPC classification number: G16H50/20 ,  G16H50/30

Abstract: Disclosed is an system for predicting end-stage renal disease complication risk based on contrastive learning, including an end-stage renal disease data preparation module, configured to extract structured data of a patient by using a hospital electronic information system and daily monitoring equipment, and process the structured data to obtain augmented structured data; and a complication risk prediction module, configured to construct a complication representation learning model and a complication risk prediction model, perform training and learning on the augmented structured data through the complication representation learning model to obtain a complication representation, and perform end-stage renal disease complication risk prediction by using the complication representation through the complication risk prediction model.

公开(公告)号：US20240013035A1

公开(公告)日：2024-01-11

申请号：US18238459

申请日：2023-08-26

Applicant: ZHEJIANG LAB ,  ZHEJIANG UNIVERSITY

Inventor： Mengxiao ZHANG ,  Huajin TANG ,  Chaofei HONG ,  Xiao WANG ,  Mengwen YUAN ,  Yujing LU ,  Wenyi ZHAO ,  Gang PAN

IPC: G06N3/049 ,  G06N3/10 ,  G06N3/08

CPC classification number: G06N3/049 ,  G06N3/10 ,  G06N3/08

Abstract: A computing platform (10), a method, and an apparatus (20) for spiking neural network (SNN) learning and simulation are provided. The computing platform (10) includes a neuron dynamics simulation module (11), a neuron conversion module (12), an SNN construction and weight learning module (13), and a neural network level parameter and weight access module (14). The neuron dynamics simulation module (11) simulates changing features of neurons. The neuron conversion module (12) performs operations on a calculation graph. The SNN construction and weight learning module (13) updates and iterates connection weights. The neural network level parameter and weight access module (14) stores overall network detail parameters.

公开(公告)号：US11868869B1

公开(公告)日：2024-01-09

申请号：US18215784

申请日：2023-06-28

Applicant: ZHEJIANG LAB

Inventor： Gang Huang ,  Wei Hua ,  Yongfu Li

IPC: G06N3/04 ,  G06N3/049

CPC classification number: G06N3/049

Abstract: The present invention relates to the field of smart grids, and provides a non-intrusive load monitoring method and device based on temporal attention mechanism. The method comprises the following steps: obtaining a total load data, an equipment load data, and corresponding sampling time of a building during a certain period of time; integrating the total load data and the equipment load data with the corresponding sampling time to obtain an enhanced total load data and an enhanced equipment load data; using a sliding window method to segment the enhanced total load data and the enhanced equipment load data, and constructing a deep learning training dataset; constructing a neural network model based on a deep learning training framework and training the model using the training dataset. The present invention can effectively extract the working time mode of the load and its inherent dependencies, thereby improving the accuracy of load monitoring.