Calculation apparatus and method for accelerator chip accelerating deep neural network algorithm

Abstract

The invention provides a calculation apparatus and method for an accelerator chip accelerating a deep neural network algorithm. The apparatus comprises a vector addition processor module, a vector function value calculator module and a vector multiplier-adder module, wherein the vector addition processor module performs vector addition or subtraction and/or vectorized operation of a pooling layer algorithm in the deep neural network algorithm; the vector function value calculator module performs vectorized operation of a nonlinear value in the deep neural network algorithm; the vector multiplier-adder module performs vector multiplication and addition operations; the three modules execute programmable instructions and interact to calculate a neuron value and a network output result of a neural network and a synaptic weight variation representing the effect intensity of input layer neurons to output layer neurons; and an intermediate value storage region is arranged in each of the three modules and a main memory is subjected to reading and writing operations. Therefore, the intermediate value reading and writing frequencies of the main memory can be reduced, the energy consumption of the accelerator chip can be reduced, and the problems of data missing and replacement in a data processing process can be avoided.

Description

technical field [0001] The invention belongs to the field of neural network algorithm and the field of computer hardware. More specifically, the present invention relates to a computing device and method of an acceleration chip for accelerating deep neural network algorithms. Background technique [0002] Artificial neural network algorithm is a research hotspot in the field of artificial intelligence since the 1980s. It abstracts the human brain neuron network from the perspective of information processing, establishes a simple model, and forms different networks according to different connection methods. It has a self-learning function, which can gradually learn to recognize and predict through training; associative storage function, with high algorithm robustness; high parallelism, with the ability to find optimal solutions at high speed, and can quickly find optimal solutions for complex problems of big data; It has strong plasticity and can fully approach any complex ...

AI Technical Summary

Problems solved by technology

A major problem that will be faced is that a large number of intermediate values ​​are generated and need to be stored, so that the required main memory space increases

At the same time, this method increases the number of times the intermediate value is stored in the main memory or read from the main memory, and the power consumption of the chip increases, which does not conform to the low-power accelerator chip design concept described above.

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