Scheduling method and device based on deep learning node calculation and storage medium

A scheduling method and deep learning technology, applied in the field of deep learning, can solve the problems of hardware idleness, unfavorable acceleration, and only applicable data flow chips, etc., and achieve the effect of asynchronous calculation process

Active Publication Date: 2020-05-22
SHENZHEN CORERAIN TECH CO LTD
View PDF4 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, there is a big difference between the data expression of the data flow architecture and the instruction set architecture: the operator granularity of the data flow architecture is much larger than that of the instruction set architecture; the operator of the data flow architecture needs to determine the arrangement order of the calculation modules according to the data dependence before calculation
This difference leads to the fact that the data flow chip is only suitable for deep learning operators, and some places with a high degree of customization still need general computing equipment for auxiliary processing.
The existence of software nodes leads to the following situations: software nodes have no hardware acceleration effect and run slowly; when running graph calculations in a single thread, the operation of software nodes will cause hardware to be idle, which is not conducive to acceleration

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Scheduling method and device based on deep learning node calculation and storage medium
  • Scheduling method and device based on deep learning node calculation and storage medium
  • Scheduling method and device based on deep learning node calculation and storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0014] Such as figure 1 As shown, an embodiment of the present invention provides a scheduling method based on deep learning node calculation, and the scheduling method based on deep learning node calculation includes steps S110 to S140.

[0015] In step S110, the nodes to be calculated of the preset neural network calculation graph are acquired.

[0016] In this embodiment, the neural network is a complex network system formed by extensive interconnection of a large number of simple processing units (also called neurons), which reflects many basic features of human brain functions and is a highly complex network system. nonlinear dynamic learning system. In the calculation graph of the actual calculation process of the neural network, the simplest calculation unit is called a calculation node, and each calculation node that has not been calculated is a node to be calculated. Obtain the nodes to be calculated in the preset neural network calculation graph, that is, insert th...

Embodiment 2

[0025] Such as figure 2 As shown, Embodiment 2 of the present invention is further optimized on the basis of Embodiment 1 of the present invention. Embodiment 2 of the present invention provides a scheduling method based on deep learning node calculation. The scheduling method based on deep learning node calculation includes Step S100 to step S422.

[0026] In step S100, the nodes to be calculated of the preset neural network calculation graph are obtained.

[0027] In this embodiment, the neural network is a complex network system formed by extensive interconnection of a large number of simple processing units (also called neurons), which reflects many basic features of human brain functions and is a highly complex network system. nonlinear dynamic learning system. In the calculation graph of the actual calculation process of the neural network, the simplest calculation unit is called a calculation node, and each calculation node that has not been calculated is a node to b...

Embodiment 3

[0052] image 3 It is a schematic structural diagram of a scheduling device based on deep learning node computing provided by Embodiment 3 of the present invention. image 3 A block diagram of an exemplary deep learning node computing-based scheduling device 12 suitable for implementing embodiments of the present invention is shown. image 3 The displayed scheduling device 12 based on deep learning node calculation is only an example, and should not impose any limitation on the functions and scope of use of the embodiments of the present invention.

[0053] Such as image 3 As shown, the scheduling device 12 based on deep learning node computing is represented in the form of a general-purpose computing device. Components of the scheduling device 12 based on deep learning node calculations may include, but are not limited to: at least one processor or processing unit 16, a storage device 28, and a bus 18 connecting different system components (including the storage device 28 ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The embodiment of the invention discloses a scheduling method and a device based on deep learning node calculation and a storage medium. The scheduling method comprises the following steps: obtainingto-be-calculated nodes of a preset neural network calculation graph; judging the node type of the node to be calculated, wherein the node type comprises a hardware calculation node and a software calculation node; scheduling the hardware computing node to a first queue under the condition that the node type is the hardware computing node, and judging whether a hardware computing power module corresponding to the hardware computing node is occupied or not; and inputting the hardware computing node into the hardware computing power module for computing under the condition that the hardware computing power module is not occupied. According to the embodiment of the invention, asynchronization of a graph reasoning process is realized, and resources of software and hardware are fully utilized.

Description

technical field [0001] Embodiments of the present invention relate to deep learning technologies, such as a scheduling method, device, and storage medium based on deep learning node calculations. Background technique [0002] Deep learning is a branch of machine learning. It is an algorithm that uses artificial neural networks as the framework to perform representation learning on data. Deep learning networks are usually trained by algorithms. In most cases, algorithm developers tend to use existing public deep learning frameworks for model training, and most public deep learning frameworks are for central processing unit (Central Processing Unit, CPU) / image processor (Graphics Processing Unit, Designed for computing devices such as GPUs. CPU / GPU adopts traditional instruction set architecture, which has low architecture efficiency and high flexibility. [0003] However, with the development of deep learning related technologies, the requirements for computing power in ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06F9/50G06N3/08
CPCG06F9/5027G06N3/08G06F2209/5018G06F9/5044G06F9/4881Y02D10/00G06F9/5016
Inventor 马恺熊超牛昕宇蔡权雄
Owner SHENZHEN CORERAIN TECH CO LTD
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap