Neural network devices based on phase change material
AssigneeSamsung Electronics Co., Ltd.
InventorsYunheub SONG, Junseop AN, Cheng LI
Life of this patent
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Abstract
A neural network device based on a phase-change material (PCM) includes a plurality of neurons that includes a first plurality of neurons in an input layer, a second plurality of neurons in a hidden layer, and a third plurality of neurons in an output layer. The neural network device includes a plurality of PCMs connecting an input line of the input layer to a connection line of the hidden layer and connecting the connection line of the hidden layer to an output line of the output layer. The first, second, and third pluralities of neurons have different structural configurations in different layers among the input layer, the hidden layer, and the output layer.
Claims11
A neural network device based on a phase-change material (PCM), the neural network device comprising:
- a plurality of neurons, the plurality of neurons including
- a first plurality of neurons in an input layer,
- a second plurality of neurons in a hidden layer, and
- a third plurality of neurons in an output layer;
- and
- a plurality of PCMs connecting an input line of the input layer to a connection line of the hidden layer and connecting the connection line of the hidden layer to an output line of the output layer,
- wherein the first, second, and third pluralities of neurons have different structural configurations in different layers among the input layer, the hidden layer, and the output layer.
An operating method of a neural network device based on a phase-change material (PCM), the neural network device including a plurality of neurons, the plurality of neurons including a first plurality of neurons in an input layer, a second plurality of neurons in a hidden layer, and a third plurality of neurons in an output layer, the operating method comprising:
- controlling the first plurality of neurons of the input layer to configure the first plurality of neurons of the input layer to operate in a forward propagation write phase and to store data;
- controlling the second plurality of neurons of the hidden layer to configure the second plurality of neurons of the hidden layer to operate in the forward propagation write phase and to convert data provided from the first plurality of neurons of the input layer;
- controlling the third plurality of neurons of the output layer to configure the third plurality of neurons of the output layer to operate in the forward propagation write phase and to convert data provided from the second plurality of neurons of the hidden layer;
- controlling the third plurality of neurons of the output layer to configure the third plurality of neurons of the output layer to operate in a forward propagation read phase and to temporarily store an output signal of the output layer;
- selecting an operation on the output layer and storing a first decision result;
- selecting an operation on the hidden layer and storing a second decision result;
- updating a weight between the hidden layer and the output layer based on controlling the third plurality of neurons of the output layer to configure the third plurality of neurons of the output layer to operate in a backward propagation read phase and controlling the second plurality of neurons of the hidden layer to configure the second plurality of neurons of the hidden layer to operate in the forward propagation read phase;
- updating a weight between the input layer and the hidden layer based on controlling the second plurality of neurons of the hidden layer to configure the second plurality of neurons of the hidden layer to operate in the backward propagation read phase and controlling the first plurality of neurons of the input layer to configure the first plurality of neurons of the input layer to operate in the forward propagation read phase;
- and
- refreshing the plurality of neurons.
Dependencies are inferred from the claim text.