Training device for memristor-based neural network and training method thereof

Abstract

The invention discloses a training device for a memristor-based neural network and a training method thereof. The neural network comprises N neuron layers which are connected one by one. The trainingmethod comprises the following steps of: inputting input data into a first neuron layer of the neural network so as to output an output result of the neural network at the Nth neuron layer, and calculating an output error of the Nth neuron layer; and counter-propagating the output error of the Nth neuron layer in a layer-by-layer manner so as to correct weight parameters between the neuron layers;and in the layer-by-layer counter-propagation process, three-valuing an output error of the mth neuron layer, and reversely inputting a voltage signal corresponding to an output result of the three-valuing operation to the mth neuron layer so as to correct a weight parameter of the mth neuron layer, wherein N is an integer greater than or equal to 3, and m is an integer greater than 1 and smallerthan N. According to the training method, the calculation ability of the memristor-based neural network is improved.

Description

technical field [0001] Embodiments of the present disclosure relate to a memristor-based neural network training device and a training method thereof. Background technique [0002] With the rapid development of the field of artificial intelligence, neural networks have achieved remarkable results in many fields such as speech and image recognition. However, with the continuous strengthening of neural network capabilities, the demand for computing resources is also increasing, and the memory wall problem in the traditional Von Neumann architecture has become a bottleneck restricting its development. In this context, the brain-inspired computing architecture based on memristor arrays is considered to be one of the most promising technologies for next-generation computing chips due to its advantages of integrated storage and computing, low energy consumption, large-scale integration and parallel operation. Contents of the invention [0003] At least one embodiment of the pre...

AI Technical Summary

Problems solved by technology

However, with the continuous strengthening of neural network capabilities, the demand for computing resources is also increasing, and the memory wall problem in the traditional von Neumann architecture has become a bottleneck restricting its development.

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