Artificial neural network compression coding device and artificial neural network compression coding method

Abstract

An artificial neural network compression coding device comprises a memory interface unit, an instruction buffer memory, a controller unit and an operation unit, wherein the operation unit is used for performing corresponding operation on data from the memory interface unit according to the instruction of the controller unit. The operation unit mainly performs three steps of: 1, multiplexing an input neuron with weight data; 2, performing an addition tree operation for adding the weighted output neurons after the first step through an addition tree or obtaining a biased output neurons through adding the output neurons and a bias; and 3, executing a function activation operation, and obtaining a final output neuron. The invention further provides an artificial neural network compression coding method. The artificial neural network compression coding device and the artificial neural network compression coding method have advantages of effectively reducing size of an artificial neural network model, improving data processing speed of the artificial neural network, effectively reducing power consumption and improving resource utilization rate.

Description

technical field [0001] The present invention relates to the technical field of artificial neural network processing, and more particularly to an artificial neural network compression encoding device and method, especially to an execution unit for executing an artificial neural network algorithm method or a device containing these execution units, and a multi-layer artificial neural network Execution units of network operations, backpropagation training algorithms and their compression encoding devices and methods, or devices including these execution units. Background technique [0002] Multi-layer artificial neural network is widely used in the fields of pattern recognition, image processing, function approximation and optimization calculation. Especially in recent years, due to the continuous deepening of research on backpropagation training algorithms and pre-training algorithms, multi-layer artificial neural networks have become more and more popular in academia and indu...

AI Technical Summary

Problems solved by technology

Since the GPU is a device specially used to perform graphics and image calculations and scientific calculations, without special support for multi-layer artificial neural network operations, it still requires a lot of front-end decoding work to perform multi-layer artificial neural network operations, which brings a lot of problems. additional cost

In addition, the GPU has only a small on-chip cache, and the model data (weights) of the multi-layer artificial neural network need to be repeatedly moved from off-chip. The off-chip bandwidth has become the main performance bottleneck, and it has brought huge power consumption overhead.

Images