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A workload proof consensus method and system for deep learning

A deep learning and workload technology, applied in the field of blockchain, can solve the problems of bookkeeping rights competition and lack of flexibility, and achieve the effect of improving algorithm efficiency, strong scalability and flexibility, and reducing costs

Active Publication Date: 2022-02-15
南京可信区块链与算法经济研究院有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] This application provides a proof-of-work consensus method and system for deep learning to solve the problems of lack of flexibility and competition for bookkeeping rights in existing technologies

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  • A workload proof consensus method and system for deep learning
  • A workload proof consensus method and system for deep learning
  • A workload proof consensus method and system for deep learning

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Embodiment Construction

[0059] see figure 1 , is an application scene diagram of a proof-of-work consensus method for deep learning in this application; the scheme of this application is applied in a blockchain network composed of demand-side nodes, training nodes, and verification nodes, where the demand-side Nodes, training nodes, and verification nodes are only divided by the different functions provided in the execution method, not a node limited in the figure can only appear as a node, it should be understood that any one in the blockchain network When a node needs to propose a consensus requirement, it can appear as a demander node, and all nodes except the demander node that raised the demand can appear as a training node. When a training node publishes a block to be verified, except for the training node All nodes other than nodes can appear as verification nodes, that is to say, demand-side nodes may also appear as verification nodes at the same time, and training nodes can also be verificat...

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Abstract

The application discloses a proof-of-work consensus method and system for deep learning, which uses smart contracts to write codes to call components in the component library to build a network structure for deep learning. If you want to perform supervised learning, you can build a network structure suitable for supervised learning. If you want to perform unsupervised learning, you can build a network structure suitable for unsupervised learning. It can be configured on demand and is very flexible. It is suitable for the training of almost all deep learning models. . It effectively solves the problem of embedding a deep learning model trainer in the consensus system in the prior art, which makes the network structure of deep learning solidified and lacks flexible configuration features.

Description

technical field [0001] The invention relates to the technical field of block chains, in particular to a workload proof consensus method and system for deep learning. Background technique [0002] At present, many public chain blockchain platforms usually use the proof-of-work consensus algorithm to determine the block bookkeeping rights, and then the nodes with higher computing power have a higher probability of obtaining bookkeeping rights. This mechanism is realized through computing power. Trust building and value consensus. However, current blockchain applications such as Bitcoin that use the proof-of-work consensus algorithm consume a lot of computing power and energy during the mining process, resulting in a waste of resources. [0003] In order to solve the above problems, some people began to adopt the method of introducing deep learning model trainers to achieve consensus on computing power. Deep learning is to learn the internal laws and representation levels of s...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F21/60G06F21/64G06Q10/06G06Q20/38G06Q20/40G06K9/62
CPCG06F21/602G06F21/64G06Q10/06315G06Q10/06393G06Q20/3825G06Q20/3829G06Q20/401G06F18/214
Inventor 石宁姜冲李天莹朱晓罡
Owner 南京可信区块链与算法经济研究院有限公司