A quantization circuit and a quantization method of a convolution neural network

Abstract

A quantization circuit and a quantization method of a convolution neural network are disclosed, The invention belongs to the technical field of artificial intelligence data processing, and comprises an original parameter pool, a comparator array, a quantization parameter calculation unit and an arithmetic operation unit. The original parameter pool is used for storing the original parameter data required for calculation of each layer of a convolution neural network, and comprises data of all channels and offset data of all convolution cores of each layer. The comparator array is used for performing statistical operation on the data in the original parameter pool, and iteratively comparing to obtain the maximum value and the minimum value of the parameters of each layer of the convolution neural network; The quantization parameter calculation unit is used for performing arithmetic operation on the maximum value and the minimum value to obtain each parameter used for model quantization;The arithmetic operation unit is used for quantifying the model, and the obtained results are expressed in an integer format of an unsigned bit specified number of bits. The invention can reduce the system power consumption through quantization, so that the deployment and operation of the convolution neural network on the terminal are more reliably guaranteed.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence data processing, in particular to a convolutional neural network quantization circuit and a quantization method. Background technique [0002] As an important direction in the development trend of artificial intelligence, convolutional neural network has been in a state of intense development. Various new models and new algorithms emerge in an endless stream, continuously injecting new impetus into this field. Among them, the increase in the depth and scale of the network model is the main development direction. In the process of continuous improvement in accuracy, the deployment and implementation of the neural network is facing great challenges. [0003] Due to the limitations of the development of mobile terminal processing and storage levels, the artificial neural network whose depth and size have doubled can only be run on processing machines with large-scale computing resour...

AI Technical Summary

Problems solved by technology

[0003] Due to the limitation of mobile terminal processing and storage level development, the artificial neural network with doubled depth and size can only be run in processing machines with large-scale computing resources, and the power consumption and running speed of the device cann...

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