Adder device, data accumulation method, and data processing device

Abstract

The invention discloses an adder device, a data accumulation method, and a data processing device. The adder device comprises a first adder module which is provided with an addition tree unit composed of a multi-stage adder array, and also provided with a first control unit, wherein the addition tree unit accumulates the data in a mode of step-by-step accumulation based on a control signal of the first control unit; a second adder module which comprises two input addition and subtraction operation units and a second control unit, and is used for the addition or subtraction operation of the input data; a shift operation module which is used for the left shift operation of the output data of the first adder module; an AND operation module which is used for the AND operation of the output data of the shift operation module and the output data of the second adder module; and a controller module which is used for controlling the data input of the first and second adder modules, controlling the shift operation of the shift operation module, and controlling the transmission of control signals of the first and second control units. Therefore, the adder device achieves the quick accumulation of data.

Description

technical field [0001] The invention belongs to the field of data processing systems, and relates to data processing of an adder device, in particular to an adder device, a data accumulation method and a data processing device including the adder device. Background technique [0002] In recent years, neural network algorithms based on the M-P neuron model have been widely used in product recommendation, image recognition and other fields. The M-P neuron model is an additive neuron model. The output value of the neuron is equal to the weighted sum of the neuron input and the result of nonlinear transformation after accumulating the bias value. That is, a large number of accumulation operations and addition operations are required in the neural network algorithm. At the same time, in the training process of neural network computing, not only a large number of accumulation operations are included, but also subtraction operations are included. [0003] When using traditional g...

AI Technical Summary

Problems solved by technology

[0003] When using traditional general-purpose processing processors to operate neural network algorithms or train a neural network, only two data can be added each time, which is inefficient.

And if fixed-point data is used for operations, it is necessary to deal with the overflow of addition

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