18113794. ELECTRONIC DEVICE PERFORMING SIMULATION OF TARGET ROW REFRESH LOGIC OF DYNAMIC RANDOM ACCESS MEMORY AND OPERATING METHOD OF ELECTRONIC DEVICE simplified abstract (SAMSUNG ELECTRONICS CO., LTD.)
ELECTRONIC DEVICE PERFORMING SIMULATION OF TARGET ROW REFRESH LOGIC OF DYNAMIC RANDOM ACCESS MEMORY AND OPERATING METHOD OF ELECTRONIC DEVICE
Organization Name
Inventor(s)
Changhwi Park of Suwon-si (KR)
ELECTRONIC DEVICE PERFORMING SIMULATION OF TARGET ROW REFRESH LOGIC OF DYNAMIC RANDOM ACCESS MEMORY AND OPERATING METHOD OF ELECTRONIC DEVICE - A simplified explanation of the abstract
This abstract first appeared for US patent application 18113794 titled 'ELECTRONIC DEVICE PERFORMING SIMULATION OF TARGET ROW REFRESH LOGIC OF DYNAMIC RANDOM ACCESS MEMORY AND OPERATING METHOD OF ELECTRONIC DEVICE
Simplified Explanation
The abstract describes an operating method for an electronic device. Here is a simplified explanation of the abstract:
- The method involves using a generator network to create an input tensor.
- The input tensor is then inputted to a target row refresh logic module to obtain a first score.
- If the first score is greater than a threshold value, the generator network and the first score are stored in an evolution pool.
- A critic network is trained based on the input tensor and the first score, as long as the number of iterations is below a maximum limit.
- The generator network is then trained based on the training result of the critic network, again within the maximum limit of iterations.
Potential applications of this technology:
- This operating method can be applied in various electronic devices, such as computers, smartphones, or IoT devices.
- It can be used in machine learning systems, where the generator network and critic network can be part of a larger neural network architecture.
Problems solved by this technology:
- The method provides a way to generate an input tensor using a generator network, which can be useful in various applications that require data generation.
- By training the critic network based on the input tensor and the first score, the method allows for the evaluation and improvement of the generated data.
Benefits of this technology:
- The method enables the generation of data using a generator network, which can be more efficient and accurate compared to manual data creation.
- By training the generator network based on the training result of the critic network, the method allows for the improvement of the data generation process over time.
Original Abstract Submitted
An operating method of an electronic device is disclosed. The operating method includes generating an input tensor by using a generator network, obtaining a first score by inputting the input tensor to a target row refresh logic module, storing a pair of the generator network and the first score in an evolution pool when the first score is greater than a threshold value, training a critic network based on the input tensor and the first score when the number of times of iteration is smaller than the maximum number of times of iteration, and training the generator network based on a training result of the critic network when the number of times of iteration is smaller than the maximum number of times of iteration.
