Model generation method and device

Abstract

The invention relates to the field of artificial intelligence. The embodiment of the invention discloses a model generation method and device. The method comprises the following steps: generating a neural network model for executing a deep learning task by sequentially executing multiple iterative operations; wherein the iterative operation comprises the steps of determining a current quantizationmethod of each network structure unit in a quantization method search space corresponding to each network structure unit of a preset neural network model based on a current reward feedback value so as to update a quantization strategy of the preset neural network model; quantifying a preset neural network model based on the updated quantification strategy; obtaining the performance of the quantized neural network model, and updating a reward feedback value; and in response to determining that the reward feedback value reaches a preset convergence condition or the number of times of the iterative operation reaches a preset threshold, determining that the current quantized neural network model is a generated neural network model for executing the deep learning task. The method can reduce the memory space occupied by the neural network model.

Description

technical field [0001] The embodiments of the present disclosure relate to the field of computer technology, specifically to the field of artificial intelligence technology, and especially to a method and device for generating a model. Background technique [0002] With the development of artificial intelligence technology and data storage technology, deep neural networks have achieved important results in many fields. There are many parameters of the neural network. As the depth increases, the memory space and bandwidth required for high-precision neural network parameters are increasing, and the computational complexity of the neural network is also increasing. Therefore, quantization is important for deep neural networks. Very important. Choosing an appropriate quantization method can effectively compress the memory space occupied by neural network parameters. However, quantization usually leads to the loss of the accuracy of the neural network model. Usually, the highe...

AI Technical Summary

Problems solved by technology

However, quantization usually leads to the loss of the accuracy of the neural network model. Usually, the higher the compression ratio of the neural network parameters, the smaller the memory space occupied and the higher the operational efficiency, but the greater the accuracy loss of the model, the accuracy of the model and the calculation Efficiency can't have both

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