Data processing method of neural network processor and neural network processor

Abstract

The invention provides a data processing method of a neural network processor and a neural network processor. The method includes the following steps that: input data and corresponding weight absolute values are added together through an adder, wherein the input data are data of the output of a previous stage, and the input data and the weight absolute values are n-element vectors; and n-term data obtained after adding the input data and the corresponding weight absolute values together are subjected to n times of first nonlinear mapping; results obtained after the first nonlinear mapping are subjected to n times of accumulation operation through an accumulator, wherein the accumulation operation includes weight sign bit-controlled adding operation and subtraction operation; and a result obtained after the n times of accumulation operation is subjected to second nonlinear mapping, so that a processing result can be obtained, and data output is carried out, wherein the second nonlinear mapping is formulated according to the rules of neural network nonlinear mapping and the inverse mapping of the first nonlinear mapping. With the method adopted, quantization efficiency can be improved, and storage requirements and bandwidth requirements of data can be decreased.

Description

technical field [0001] The embodiments of the present invention relate to the technical field of electronic chips, and in particular to a data processing method of a neural network processor and the neural network processor. Background technique [0002] Neural networks and deep learning algorithms have been very successfully applied and are in the process of rapid development. The industry generally expects that new computing methods will help realize more common and complex intelligent applications. Neural networks and deep learning algorithms have made outstanding achievements in the field of image recognition applications in recent years. Therefore, the industry has begun to pay attention to the optimization and high-efficiency implementation of neural networks and deep learning algorithms, such as facebook, Qualcomm, baidu, google and other companies All have invested in the research of neural network optimization algorithms. Qualcomm has released a plan to integrate n...

AI Technical Summary

Problems solved by technology

[0005] Embodiments of the present invention provide a data processing method of a neural network processor and a neural network processor to solve the problems of high data storage requirements and bandwidth requirements and low computational efficiency in existing processing methods

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