• Patent Title: Method of neural network model computation-oriented intermediate representation by constructing physical computation graph, inferring information of input and output tensor edges of each node therein, performing memory optimization on tensor edges, and optimizing physical computation graph
  • Application No.: US17714454
    Application Date: 2022-04-06
  • Publication No.: US11823053B2
    Publication Date: 2023-11-21
  • Inventor: Hongsheng WangWei HuaWeiqiang JiaHujun Bao
  • Applicant: ZHEJIANG LAB
  • Applicant Address: CN Hangzhou
  • Assignee: ZHEJIANG LAB
  • Current Assignee: ZHEJIANG LAB
  • Current Assignee Address: CN Hangzhou
  • Agency: IPro, PLLC
  • Priority: CN 2210144108.2 2022.02.17
  • Main IPC: G06N3/082
  • IPC: G06N3/082 G06N3/04
Method of neural network model computation-oriented intermediate representation by constructing physical computation graph, inferring information of input and output tensor edges of each node therein, performing memory optimization on tensor edges, and optimizing physical computation graph
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.
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