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A neuromorphic system based on resistive switching devices and adaptive-excitation neurons and its implementation method

A technology of resistive devices and morphological systems, applied in neural learning methods, physical implementation, biological neural network models, etc., can solve problems such as limiting the development and application of neuromorphic systems, independent training, unsupervised learning, etc. Flexibility, area reduction effect

Active Publication Date: 2019-09-13
PEKING UNIV
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AI Technical Summary

Problems solved by technology

Due to the challenging design of peripheral circuits, existing systems cannot be independently trained without a computer, or can only perform unsupervised learning, which severely limits the development and application of neuromorphic systems.

Method used

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  • A neuromorphic system based on resistive switching devices and adaptive-excitation neurons and its implementation method
  • A neuromorphic system based on resistive switching devices and adaptive-excitation neurons and its implementation method
  • A neuromorphic system based on resistive switching devices and adaptive-excitation neurons and its implementation method

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Embodiment Construction

[0030] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided for more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0031] Such as figure 1 As shown, the trainable neuromorphic system based on resistive switching devices involved in the present invention mainly includes the following parts: 1, cross array of resistive switching devices; 2, front neurons; 3, rear neurons; 4, global dynamic threshold control circuit ;5. Control logic module; 6. Voltage regulation module; 7. Sample input; 8. Label input; 9. Result output.

[0032] Among them, sam...

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Abstract

The invention discloses a neuromorphic system based on variable-resistance devices and adaptive-excited neurons and a realization method. The system includes a crossed array of the variable-resistance devices, the anterior neurons, the posterior neurons, a global dynamic threshold value control circuit, a control logic module, a voltage regulation module, a sample input, a tag input and a result output. According to the system, the variable-resistance devices are used as electronic synapses, and a new structure and operation mode of the adaptive-excited neurons are provided, thus the system is optimized on area and operation aspects, and training problems faced by the same type of systems are solved.

Description

technical field [0001] The invention adopts a novel resistive variable device to design a trainable neuromorphic system, is a parallel hardware implementation of an artificial neural network, and belongs to the technical field of integrated circuits, artificial intelligence and neural network systems. Background technique [0002] Neural networks are one of the most widely used and accomplished techniques in the field of artificial intelligence. The current neural network algorithm implementation includes two aspects: software implementation and hardware implementation. Among them, there is a Von Neumann bottleneck in the modern computer on which the software implementation depends, that is, in the Von Neumann structure, the computing module and the storage unit are separated, and the CPU must first read data from the storage unit when executing commands, and the central Frequent data transmission between the processor and the memory needs to pass through the bus, and the l...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N3/06G06N3/08
Inventor 康晋锋江宇宁黄鹏周正柳晨韩润泽刘晓彦刘力锋
Owner PEKING UNIV
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