Huawei Technologies Co., Limited

Shenzhen Yinwang Intelligent Technologies Co., Ltd.

Wulong Liu

Yu Wang

Bin Wang

Huimin Zhang

Chen Chen

Jun Yao

Fenglong Song

Xijun Xue

Qiang Gu

Jun Qian

Yuzheng Zhuang

Ming Cheng

Lixue Xia

Shixiong Kai

This application provides a method for determining an automatic parking strategy. The method includes: determining, a target parking action corresponding to a current parking stage performing the target parking action; obtaining feedback information, where the feedback information is used to indicate whether a result of performing the target parking action reaches a predetermined objective, and the predetermined objective is a predetermined position of the vehicle relative to a target parking spot, and/or the predetermined objective is a status of the vehicle in the parking process; and updating the automatic parking strategy based on the feedback information. In the foregoing method, the entire parking process is divided into several parking stages, and a control strategy is obtained by using a different method at each stage. This can increase a success rate of automatic parking in a complex parking scenario.

Method and apparatus for determining automatic parking strategy

This application provides a method, a terminal-side device, and a cloud-side device for data processing and a terminal-cloud collaboration system. The method includes: sending, by the terminal-side device, a request message to the cloud-side device; receiving, by the terminal-side device, a second neural network model that is obtained by compressing a first neural network model and that is sent by the cloud-side device, where the first neural network model is a neural network model on the cloud-side device that is used to process the cognitive computing task, and a hardware resource required when the second neural network model runs on the terminal-side device is within an available hardware resource capability range of the terminal-side device; and processing, by the terminal-side device, the cognitive computing task based on the second neural network model.

Method, terminal-side device, and cloud-side device for data processing and terminal-cloud collaboration system

A neural network training method includes inputting neuron input values of a neural network to the RRAM, and performing calculation for the neuron input values based on filters in the RRAM, to obtain neuron output values of the neural network, performing calculation based on kernel values of the RRAM, the neuron input values, the neuron output values, and backpropagation error values of the neural network, to obtain backpropagation update values of the neural network, comparing the backpropagation update values with a preset threshold, and when the backpropagation update values are greater than the preset threshold, updating the filters in the RRAM based on the backpropagation update values.

Neural network training method and apparatus

A peripheral circuit includes a data preparation circuit, configured to selectively import, to a row or column of the resistive random access memory (RRAM) crossbar array based on a first control signal, preprocessed data obtained by first preprocessing on first data that is input into the data preparation circuit, a data selection circuit, configured to selectively export second data from the row or column of the RRAM crossbar array based on a second control signal, and perform second preprocessing on the second data to obtain third data, a data reading circuit, configured to: perform a weight update control operation, and perform a max pooling operation on fourth data that is input into the data reading circuit, to obtain fifth data, and a reverse training computation circuit, configured to calculate an error and a derivative of sixth data that is input into the reverse training computation circuit.

Peripheral circuit and system supporting RRAM-based neural network training

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Beijing, China

Wulong Liu