Huawei Technologies Co., Limited

Future Wei Technologies, Inc.

Zhicheng Sui

Li Zhou

Qinghua Yu

Mohan Liu

Yufei Wang

Hongrui Jiang

Lei Zhao

Victor Yu Liu

Yanming Li

Chuanjun Wu

Xinchao Liu

Lixun Bai

This application relates to the field of artificial intelligence technologies, and discloses a machine learning model search method, a related apparatus, and a device. In the method, before model search and quantization, a plurality of single bit models are generated based on a to-be-quantized model, and evaluation parameters of layer structures in the plurality of single bit models are obtained. Further, after a candidate model selected from a candidate set is trained and tested, to obtain a target model, a quantization weight of each layer structure in the target model may be determined based on a network structure of the target model and evaluation parameters of all layer structures in the target model, a layer structure with a maximum quantization weight in the target model is quantized, and a model obtained through quantization is added to the candidate set.

Machine learning model search method, related apparatus, and device

A neural network model processing method includes obtaining a first low-bit neural network model through training, where the model includes a first operation layer and a second operation layer. Each operation layer includes at least one operation. Values/a value of a parameter and/or data used for the operation are/is represented by using N bits, and N is a positive integer less than 8. The neural network model processing method further includes compressing the model to obtain a second low-bit neural network model, where the compressed model includes a third operation layer. The third operation layer is equivalent to the first operation layer and the second operation layer, and an operation layer other than the third operation layer in the at least one operation layer is the same as an operation layer other than the first operation layer and the second operation layer in the at least two operation layers.

Neural network model processing method and apparatus

A voice information processing method includes: receiving, by a terminal, voice information, and converting the voice information into text information; obtaining a field probability that the text information belongs to each of preset M event fields; obtaining a prior probability that the text information belongs to each of N event fields, where N&#x2264;M; obtaining confidence that the text information belongs to each of the N event fields; calculating, based on the field probability, the prior probability, and the confidence that the text information belongs to each of the N event fields, values of N probabilities that the text information separately belongs to the N event fields; and outputting a semantic understanding result of performing semantic understanding based on an event field corresponding to a largest probability value in the N probability values.

Voice information processing method and apparatus, and terminal

The present disclosure provides a network service establishment method, an orchestration control center, and a network system. The method includes: obtaining, by means of screening according to QoP information and SLA information of a service, all target service protection types that satisfy the QoP information and/or the SLA information of the service for each layer network in a multi-layer network; determining, according to all the target service protection types, at least one group of multi-layer service protection types that satisfy the QoP information and the SLA information of the service for the multi-layer network; determining a group of target multi-layer service protection types of the service according to network topology information and resource usage of each layer network from the at least one group of the multi-layer service protection types; and sending an instruction message that carries the target multi-layer service protection types to a network controller.

Network service establishment method, orchestration control center, and network system

The present application provides a method for planning a recovery resource for resisting N-time faults and an optical transmission device, and the method includes: planning, on an optical transmission device according to preset network planning information, a recovery resource for resisting (N&#x2212;1)-time faults for preset (N&#x2212;1)-time faults, and the recovery resource for resisting (N&#x2212;1)-time faults is an optimal recovery resource corresponding to each interrupted service during the preset (N&#x2212;1)-time faults; and planning, by the optical transmission device according to the network planning information and the recovery resource for resisting (N&#x2212;1)-time faults, a recovery resource for resisting N-time faults for preset N-time faults, where the recovery resource for resisting N-time faults is a network-wide optimal recovery resource corresponding to interrupted services during the N-time faults. According to the present application, recovery resource costs can be reduced, and recovery resource planning reliability can be improved.

Method for planning recovery resource for resisting N-time faults and optical transmission device

A method for protecting a bi-connected network against a dual link failure, the method comprising finding a plurality of cut-pairs for a data flow, finding a work path and a primary backup path for the data flow, determining the cut-pairs that overlap both the work path and the primary backup path, merging the cut-pairs that overlap the work path and the primary backup path into a plurality of cut-groups, wherein each cut-group comprises a first half and a second half, and constructing a plurality of partially disjoint backup paths based on the cut-groups, wherein one of the partially disjoint backup paths protects against the dual link failure that causes a failure on the second half of a first cut-group and a failure on the first half of a second cut-group.

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Shanghai, China

Zhicheng Sui