AI calculation graph sorting method and device, equipment and storage medium

A sorting method and computing graph technology, applied in the direction of multi-program device, computing, program control design, etc., can solve the problems of low delay, high utilization rate, data flow architecture and instruction set architecture are not the same architecture, etc., to achieve improvement The effect of chip performance

Pending Publication Date: 2020-10-09
SHENZHEN CORERAIN TECH CO LTD
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AI Technical Summary

Problems solved by technology

[0004] In recent years, an artificial intelligence chip developed based on the data flow architecture has attracted much attention because of its high utilization rate and low delay. However, the data flow architecture and the instruction set architecture are not the same architecture. The instruction control unit and cache mechanism of the instruction set architecture It cannot be reused in the data flow architecture, and the artificial intelligence chip developed based on the data flow architecture does not have an instruction control unit, and only accepts the order of the pre-optimized calculation graph for execution. Therefore, there is an urgent need for a processing method to advance the calculation graph Optimize the ordering of nodes so that the artificial intelligence chip developed based on the data flow architecture can normally operate on the calculation graph

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  • AI calculation graph sorting method and device, equipment and storage medium
  • AI calculation graph sorting method and device, equipment and storage medium
  • AI calculation graph sorting method and device, equipment and storage medium

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Embodiment 1

[0056] Figure 1AIt is a schematic flowchart of the AI ​​calculation graph sorting method provided by Embodiment 1 of the present invention. This embodiment is applicable to the node sorting of the deep learning model calculation graph based on the data flow architecture. This method can be implemented by the sorting device of the AI ​​calculation graph, and It can be realized by means of hardware or software. Such as Figure 1A As shown, the AI ​​calculation graph sorting method provided by Embodiment 1 of the present invention includes:

[0057] S110. Obtain a computing graph based on the data flow architecture, where the computing graph includes multiple computing nodes.

[0058] Specifically, the calculation graph based on the data flow architecture refers to the calculation graph of the deep learning model developed based on the data flow architecture. Computational graph is a kind of computing process with Directed Acyclic Graph (DAG) as data structure, which includes m...

Embodiment 2

[0070] Figure 2A It is a schematic flowchart of the method for sorting AI calculation graphs provided by Embodiment 2 of the present invention. This embodiment is a further refinement of the foregoing embodiments. Such as Figure 2A As shown, the AI ​​calculation graph sorting method provided by Embodiment 2 of the present invention includes:

[0071] S210. Acquire a computing graph based on the data flow architecture, where the computing graph includes multiple computing nodes.

[0072] S220. Perform topological sorting on the computation graph to obtain a first arrangement order of the computation graph.

[0073] S230. Determine multiple branch start nodes and multiple branches corresponding to each branch start node.

[0074] Specifically, the calculation graph of the deep learning model is a directed acyclic graph, usually with a large number of calculation nodes and a complex structure, so there are also multiple branch start nodes, and each branch is based on its cor...

Embodiment 3

[0107] image 3 A schematic structural diagram of the device for sorting AI calculation graphs provided by Embodiment 3 of the present invention. This embodiment is applicable to the sorting of nodes in the calculation graphs of deep learning models based on data flow architecture. The device can implement the AI ​​provided by any embodiment of the present invention. The sorting method of the calculation graph has the corresponding functional structure and beneficial effects of the realization method. For the content not described in detail in this embodiment, please refer to the description of any method embodiment of the present invention.

[0108] Such as image 3 As shown, the AI ​​calculation graph sorting device provided by Embodiment 3 of the present invention includes: a calculation graph acquisition module 310, a topological sorting module 320, a branch sorting module 330, and a target sorting order determination module 340, wherein:

[0109] The computing graph acqu...

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Abstract

The embodiment of the invention discloses an AI calculation graph sorting method and device, equipment and a storage medium. The method comprises the steps: obtaining a calculation graph based on dataflow architecture, and enabling the calculation graph to comprise a plurality of calculation nodes; performing topological sorting on the calculation graph to obtain a first arrangement sequence of the calculation graph; determining a second arrangement sequence of a plurality of branch computing nodes according to the branch sequence of the plurality of branches in the calculation graph; and replacing the arrangement sequence of the branch computing nodes in the first arrangement sequence with a second arrangement sequence corresponding to the branch computing nodes to obtain a target arrangement sequence of the calculation graph. According to the embodiment of the invention, topological sorting and branch sorting are carried out on the calculation graph, so that sorting of the calculation graph based on the data flow architecture is realized, the execution sequence of each calculation node in the calculation graph can be uniquely determined, and the chip performance based on the data flow architecture is improved.

Description

technical field [0001] The embodiments of the present invention relate to the technical field of artificial intelligence, and in particular to a sorting method, device, device and storage medium for AI calculation graphs. Background technique [0002] The deep learning model is essentially a computational graph, such as the convolutional neural network model, which is essentially a directed acyclic computational graph. The computational graph contains a large number of computational nodes, each of which represents a computational operation, and has input dependencies. , that is, the input nodes of the current computing node are all calculated before the current computing node can be executed. [0003] At present, the development of artificial intelligence chips is mostly based on the instruction set architecture of the current CPU (Central Processing Unit, central processing unit) and GPU (Graphics Processing Unit, graphics processing unit), and the CPU and GPU usually have ...

Claims

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Application Information

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IPC IPC(8): G06F9/48G06F9/50
CPCG06F9/4881G06F9/5027Y02D10/00
Inventor 邹伟熊超蔡权雄牛昕宇
Owner SHENZHEN CORERAIN TECH CO LTD
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