Neuron analog circuit, driving method thereof and neural network device

Abstract

The invention discloses a neuron analog circuit, a driving method thereof and a neural network device. The neuron analog circuit comprises an integrating circuit and a threshold adjusting circuit, andthe integrating circuit is configured to change the voltage of the first node in one direction in response to the excitation signal; the threshold adjusting circuit comprises a first memristor, a second memristor and a monostable circuit, wherein the output end and the input end of the monostable circuit are connected with a second node and a third node respectively; the first end of the first memristor is controlled by the voltage of the first node, and the second end of the first memristor is connected with the third node; the first end of the second memristor is controlled by the voltage of the second node, and the second memristor is configured to change the resistance value of the second memristor according to the voltage difference between the first end and the second end of the second memristor. The neuron simulation circuit enables excitability of neurons to be adaptively adjusted according to network activities, and is suitable for integration of a large-scale spiking neuralnetwork hardware system.

Description

technical field [0001] Embodiments of the present disclosure relate to a neuron simulation circuit, a driving method thereof, and a neural network device. Background technique [0002] With the rapid development of information technology, the improvement of operating speed and the reduction of energy consumption have become the bottleneck of the development of the traditional von Neumann computing architecture. Features such as adaptation are considered to be the breakthrough direction for high-performance computers. In the human brain, neurons and synapses are the most important and most numerous neural units. Therefore, building a brain-like computing system requires a large number of neuron-like and synapse-like electronic devices. In addition to the functions of simulating neurons and synapses, these electronic devices also need to have small area, low power consumption, and high speed. performance. [0003] At present, in the brain-inspired computing system, neuron-l...

AI Technical Summary

Problems solved by technology

[0003] At present, in the brain-inspired computing system, neuron-like functions need to be realized through circuit design. Due to the limitation of hardware resources, neuron-like electronic devices have defects such as high energy consumption and inability to handle complex tasks, which further limit the brain-like computing system. development of

Images