Device and method for executing forward operation of artificial neural network

Abstract

The invention provides a device and a method for executing a forward operation of an artificial neural network. The device comprises the components of a floating point data statistics module which is used for performing statistics analysis on varies types of required data and obtains the point location of fixed point data; a data conversion unit which is used for realizing conversion from a long-bit floating point data type to a short-bit floating point data type according to the point location of the fixed point data; and a fixed point data operation module which is used for performing artificial neural network forward operation on the short-bit floating point data. According to the device provided by the invention, through representing the data in the forward operation of the multilayer artificial neural network by short-bit fixed points, and utilizing the corresponding fixed point data operation module, forward operation for the short-bit fixed points in the artificial neural network is realized, thereby greatly improving performance-to-power ratio of hardware.

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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