An apparatus and method for performing an artificial neural network forward operation

Abstract

The invention discloses a device and a method for executing forward operation of an artificial neural network, and the device comprises a floating point data statistics module which is used for carrying out statistics analysis on various types of required data, and obtaining the decimal point position Point locality of fixed point data; a data conversion unit which is used for realizing conversionfrom the long-bit floating point data type to the short-bit fixed point data type according to the decimal point position of the fixed point data; And a fixed-point data operation module which is used for carrying out artificial neural network forward operation on the short-digit fixed-point data. According to the device disclosed by the invention, the data in the forward operation of the multilayer artificial neural network is represented by using the short-digit fixed points, and the corresponding fixed-point operation module is used, so that the short-digit fixed-point forward operation ofthe artificial neural network is realized, and the performance power consumption ratio of hardware is greatly improved.

Description

technical field [0001] The present invention relates to the technical field of artificial neural network operation, and more particularly relates to a device and method for performing forward operation of artificial neural network. Background technique [0002] Multi-layer artificial neural networks are widely used in the fields of pattern recognition, image processing, function approximation, and optimization calculations. In recent years, multi-layer artificial neural networks have been favored by academic circles and industry is getting more and more attention. [0003] Traditional artificial neural network forward operations usually use 32-bit floating-point data types for data storage, but in most forward operations of artificial neural networks, the same type of data will be concentrated within a certain data range. However, the range of data that can be represented by 32-bit floating point is much larger than the data range of the same type of data in the forward ope...

AI Technical Summary

Problems solved by technology

[0003] Traditional artificial neural network forward operations usually use 32-bit floating-point data types for data storage, but in most forward operations of artificial neural networks, the same type of data will be concentrated within a certain data range. However, the data range that can be represented by 32-bit floating point is much larger than the data range of the same type of data in the forward operation. There is a lot of redundancy in the use of 32-bit floating point data type, which increases the area overhead of hardware.

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