Electronic device performing simulation of target row refresh logic of dynamic random access memory and operating method of electronic device

An electronic device comprising:

• a processor;
• and
• a memory,
• wherein the processor is configured to execute a simulator performing simulation of target row refresh logic of a dynamic random access memory by using the memory,
• wherein the simulator includes:
• a first module configured to execute an algorithm of the target row refresh logic and to output a risk level as a first score;
• and
• a second module configured to perform the simulation by using the first module,
• wherein the second module includes:
• a generator pool including a plurality of generator networks each configured to generate an input tensor of the first module;
• and
• a critic network configured to be trained to replicate the first module and to infer a second score from the input tensor,
• wherein each of the plurality of generator networks is repeatedly trained together with the critic network, and
• wherein, in each iteration where the training is repeated, when the first score is greater than a threshold value, a generator network corresponding to the first score is stored in an evolution pool together with the first score.

An operating method of an electronic device which includes a processor performing simulation of target row refresh logic of a dynamic random access memory, the method comprising:

• storing generator networks in an evolution pool by repeatedly training each of a plurality of generator networks and a critic network;
• and
• repeatedly training each generator network of the evolution pool and the critic network,
• wherein the training of each generator network and the critic network includes:
• generating an input tensor by using each generator network;
• generating a first score by using a target row refresh logic module based on the input tensor;
• storing each generator network in the evolution pool when the first score is greater than a threshold value;
• generating a second score from the input tensor by using the critic network;
• training the critic network such that the first score is generated from the input tensor;
• and
• training each generator network such that the second score reaches the first score by the input tensor.