Method and apparatus with optimization for deep learning model

An apparatus with quantization for a deep learning model, comprising:

• one or more processors configured to:
• determine a second model by quantizing a first model based on a quantization parameter;
• determine a real value of a multi optimization target parameter by testing the second model;
• determine a loss function based on the real value of the multi optimization target parameter, an expected value of the multi optimization target parameter, and by assigning a penalty in response to a real value of a specific target parameter of the multi optimization target parameter exceeding a constraint value of the multi optimization target parameter, wherein a value of the penalty is determined based on a penalty factor and a difference value between the real value of the specific target parameter and the constraint value;
• update the quantization parameter based on the loss function and use the second model as the first model;
• and
• in response to a result of updating the quantization parameter satisfying a preset condition, determine an optimal quantization parameter and use, as a final quantization model, the first model that is quantized based on the optimal quantization parameter, wherein the determining of the second model comprises:
• determining a simulation quantization model using the first model, for mapping the quantization parameter with each operator to be quantized in the first model;
• and
• determining the second model by executing model reconfiguration on the simulation quantization model based on a quantization coefficient, the quantization coefficient comprising a scale factor and a zero point.

An apparatus with quantization for a deep learning model, comprising:

• one or more processors configured to:
• determine a second model by quantizing, for each of a plurality of layers of a first model, either one or both of an activation value and a weight based on a quantization parameter;
• determine a value of a multi optimization target parameter based on the second model;
• determine a loss based on the value of the multi optimization target parameter, an expected value of the multi optimization target parameter, and by assigning a penalty in response to a real value of a specific target parameter of the multi optimization target parameter exceeding a constraint value of the multi optimization target parameter, wherein a value of the penalty is determined based on a penalty factor and a difference value between the real value of the specific target parameter and the constraint value;
• update the quantization parameter based on the loss and use the second model as the first model;
• and
• update the first model to execute quantization based on the updated quantization parameter,
• wherein the determining of the second model comprises:
• determining a simulation quantization model using the first model, for mapping the quantization parameter with each operator to be quantized in the first model;
• and
• determining the second model by executing model reconfiguration on the simulation quantization model based on a quantization coefficient, the quantization coefficient comprising a scale factor and a zero point.