Patent ID: 11948074
Assignee: SAMSUNG ELECTRONICS CO., LTD.
Field: Computer technology (Electrical engineering)
Classification: CPC G  B | IPC B  G

Claim 19:
20. A processor-implemented method of training a neural network, the method comprising:
initializing a weight of a current layer of the neural network, a weight quantization parameter related to the weight, and an activation quantization parameter related to an output activation map output from the current layer, where the neural network includes a first representation bit number and a second representation bit number that are pre-trained; and
iteratively training the neural network, including:
calculating a loss as a calculated loss based on the first representation bit number related to the weight, the second representation bit number related to the output activation map, the weight, the weight quantization parameter, and the activation quantization parameter, based on training data; and
train the neural network by updating the weight, the weight quantization parameter, and the activation quantization parameter based on the calculated loss,

wherein a first parameter is dependent on first and second thresholds derived from the output activation map, and a second parameter That is dependent on the first and second thresholds, and
wherein the calculating of the loss includes quantizing the activation quantization parameter dependent on the first parameter and the second parameter.