Artificial neural network calculating device and method for sparse connection

Abstract

An artificial neural network calculating device for sparse connection comprises a mapping unit used for converting input data into the storage mode that input nerve cells and weight values correspond one by one, a storage unit used for storing data and instructions, and an operation unit used for executing corresponding operation on the data according to the instructions. The operation unit mainly executes three steps of operation, wherein in the first step, the input nerve cells and weight value data are multiplied; in the second step, addition tree operation is executed, the weighted output nerve cells processed in the first step are added level by level through an addition tree, or the output nerve cells are added with offset to obtain offset-added output nerve cells; in the third step, activation function operation is executed, and the final output nerve cells are obtained. By means of the device, the problems that the operation performance of a CPU and a GPU is insufficient, and the expenditure of front end coding is large are solved, support to a multi-layer artificial neural network operation algorithm is effectively improved, and the problem that memory bandwidth becomes a bottleneck of multi-layer artificial neural network operation and the performance of a training algorithm of the multi-layer artificial neural network operation is solved.

Description

technical field [0001] The present invention relates to the technical field of data processing, and more particularly to an artificial neural network computing device and method for sparse connections. Background technique [0002] Artificial Neural Networks (ANNs), referred to as Neural Networks (NNs) for short, is an algorithmic mathematical model that imitates the behavioral characteristics of animal neural networks and performs distributed parallel information processing. This kind of network depends on the complexity of the system, and achieves the purpose of processing information by adjusting the interconnection relationship between a large number of internal nodes. The algorithm used in neural networks is vector multiplication, and symbolic functions and their various approximations are widely used. [0003] Just like the neural network in the brain, the neural network consists of some interconnected nodes, such as figure 1 As shown, each circle represents a neuron...

AI Technical Summary

Problems solved by technology

However, since the GPU is a device specially used to perform graphics and image operations and scientific computing, it does not have special support for sparse artificial neural network operations, and still requires a lot of front-end decoding work to perform sparse artificial neural network operations, which brings a lot of problems. additional overhead

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.

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