中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

35 records (took 0.021 seconds)

    Convolutional neural network computing method and system based on weight kneading
    31.
    发明授权
    Convolutional neural network computing method and system based on weight kneading 有权

    公开(公告)号：US12271807B2

    公开(公告)日：2025-04-08

    申请号：US17250892

    申请日：2019-05-21

    Applicant: Institute of Computing Technology, Chinese Academy of Sciences

    Inventor： Xiaowei Li ,  Xin Wei ,  Hang Lu

    IPC: G06N3/048 ,  G06F5/01 ,  G06F7/50 ,  G06F7/544 ,  G06F17/16 ,  G06N3/04 ,  G06N3/063 ,  H03M7/40

    Abstract: Disclosed embodiments relate to a convolutional neural network computing method and system based on weight kneading, comprising: arranging original weights in a computation sequence and aligning by bit to obtain a weight matrix, removing slack bits in the weight matrix, allowing essential bits in each column of the weight matrix to fill the vacancies according to the computation sequence to obtain an intermediate matrix, removing null rows in the intermediate matrix, obtain a kneading matrix, wherein each row of the kneading matrix serves as a kneading weight; obtaining positional information of the activation corresponding to each bit of the kneading weight; divides the kneading weight by bit into multiple weight segments, processing summation of the weight segments and the corresponding activations according to the positional information, and sending a processing result to an adder tree to obtain an output feature map by means of executing shift-and-add on the processing result.

    Method and system for processing neural network
    32.
    发明授权
    Method and system for processing neural network 有权

    公开(公告)号：US11580367B2

    公开(公告)日：2023-02-14

    申请号：US16079525

    申请日：2016-08-09

    Applicant: Institute of Computing Technology, Chinese Academy of Sciences

    Inventor： Zidong Du ,  Qi Guo ,  Tianshi Chen ,  Yunji Chen

    IPC: G06N3/06 ,  G06F7/57 ,  G06F15/78 ,  G06N3/04 ,  G06N3/063

    Abstract: The present disclosure provides a neural network processing system that comprises a multi-core processing module composed of a plurality of core processing modules and for executing vector multiplication and addition operations in a neural network operation, an on-chip storage medium, an on-chip address index module, and an ALU module for executing a non-linear operation not completable by the multi-core processing module according to input data acquired from the multi-core processing module or the on-chip storage medium, wherein the plurality of core processing modules share an on-chip storage medium and an ALU module, or the plurality of core processing modules have an independent on-chip storage medium and an ALU module. The present disclosure improves an operating speed of the neural network processing system, such that performance of the neural network processing system is higher and more efficient.

    Fractal-tree communication structure and method, control apparatus and intelligent chip
    33.
    发明授权
    Fractal-tree communication structure and method, control apparatus and intelligent chip 有权

    公开(公告)号：US10805233B2

    公开(公告)日：2020-10-13

    申请号：US15781686

    申请日：2016-06-17

    Applicant: INSTITUTE OF COMPUTING TECHNOLOGY, CHINESE ACADEMY OF SCIENCES

    Inventor： Huiying Lan ,  Tao Luo ,  Shaoli Liu ,  Shijin Zhang ,  Yunji Chen

    IPC: H04L12/911 ,  G06F15/173 ,  H04L12/44 ,  H04L12/755 ,  H04L12/933

    Abstract: A communication structure comprises: a central node that is a communication data center of a network-on-chip and used for broadcasting or multicasting communication data to a plurality of leaf nodes; a plurality of leaf nodes that are communication data nodes of the network-on-chip and used for transmitting the communication data to the central node; and forwarder modules for connecting the central node with the plurality of leaf nodes and forwarding the communication data, wherein the plurality of leaf nodes are divided into N groups, each group having the same number of leaf nodes, the central node is individually in communication connection with each group of leaf nodes by means of the forwarder modules, the communication structure is a fractal-tree structure, the communication structure constituted by each group of leaf nodes has self-similarity, and the forwarder modules comprises a central forwarder module, leaf forwarder modules, and intermediate forwarder modules.

Patent Agency Ranking