Multi-stage associative memory circuit based on non-molten phase change device

Abstract

The invention discloses a multi-stage associative memory circuit based on a non-molten phase change device, which belongs to the field of artificial neural networks, and comprises: N + 1 input nodes respectively used for receiving non-conditional stimulation signals and conditional stimulation signals of each stage; N (N + 1) / 2 interconnection modules, wherein one interconnection module is connected between any two input nodes so as to couple signals received by the input nodes in pairs, each interconnection module comprises two branches which are reversely connected in parallel, and each branch comprises a non-molten phase change device and a diode which are connected in series. Each input node is also connected with a resistance synapse and an output neuron module in sequence, and is used for carrying out integration on a coupled signal in the input node and respectively outputting an unconditional response signal and each level of stimulation response signal according to the size between an integration result and a neuron threshold voltage. The invention is simple in structure, does not need a complex peripheral control circuit, and can simulate the obtaining and fading processes of multi-stage conditioned reflex in classical conditioned reflex.

Description

technical field [0001] The invention belongs to the field of artificial neural network, and more specifically relates to a multi-level associative memory circuit based on a non-melting phase change device. Background technique [0002] Associative memory is the ability of organisms to learn and remember the connections between unrelated things, and it is an important way for humans and various organisms to learn new information in their daily lives. In biology, the plasticity of synapses is the basis of associative memory. After irrelevant stimulus signals are input into the neural network multiple times in a certain way, new high-strength connections will be formed between them, that is, the synaptic weights of connections under certain learning rules will be enhanced. In order to complete the associative memory function, most traditional artificial neural networks use dozens of operational amplifiers, transistors and other components to realize electronic neurons and elec...

AI Technical Summary

Problems solved by technology

Second-level associative memory and more-level associative memory are important foundations of associative learning. At present, there is basically no related work on the use of memristor networks to achieve multi-level associative memory.

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