A convolution neural network-on-chip learning system based on nonvolatile memory

Abstract

The invention discloses a convolution neural network on-chip learning system based on non-volatile memory, comprising an input module, a convolution neural network module, an output module and a weight update module. The on-chip learning of the convolution neural network module utilizes the characteristic that the conductance of the memristor changes with the applied pulse to realize the synapticfunction, and the convolution kernel value or the synaptic weight value is stored in the memristor unit. The input module converts the input signal into the voltage signal required by the convolutional neural network module. The convolutional neural network module transforms the input voltage signal layer by layer and transmits the result to the output module to get the output of the network. Theweight updating module adjusts the conductance value of the memristor in the convolutional neural network module according to the result of the output module, and updates the convolution core value orsynaptic weight value of the network. The invention aims at realizing the on-chip learning of the convolution neural network, realizing the on-line processing of the data, and realizing the requirements of high speed, low power consumption and low hardware cost based on the high parallelism of the network.

Description

technical field [0001] The present invention relates to the technical field of artificial neural network, and more specifically, relates to a convolutional neural network on-chip learning system based on a non-volatile memory. Background technique [0002] The artificial neural network is a structure similar to the synaptic connection of the brain. It is an algorithmic mathematical model that imitates the behavioral characteristics of the animal neural network and performs information processing on a distributed parallel information processing model. Among many machine learning algorithms, neural networks have wide applicability and strong robustness. 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. [0003] As one of the most important algorithms of deep learning, convolutional neural network has great advantages in large-s...

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Problems solved by technology

[0004] In view of the defects of the prior art, the purpose of the present invention is to solve the von Neumann bottleneck encountered by the existing computer to realize the convolutional neural network. The separation of calculation and storage is quite time-consuming and low-speed, and will lead to huge hardware costs. At the same time The off-chip learning realized by computer can only realize the specific functions of the pre-training, and cannot solve the technical problems flexibly in real time

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