Intel corporation (20240241842). ACCELERATED DRAM (DYNAMIC RANDOM ACCESS MEMORY) TRAINING simplified abstract
Contents
ACCELERATED DRAM (DYNAMIC RANDOM ACCESS MEMORY) TRAINING
Organization Name
Inventor(s)
Saravanan Sethuraman of Portland OR (US)
Tonia M. Rose of Wendell NC (US)
Caroline Grimes of Raleigh NC (US)
ACCELERATED DRAM (DYNAMIC RANDOM ACCESS MEMORY) TRAINING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240241842 titled 'ACCELERATED DRAM (DYNAMIC RANDOM ACCESS MEMORY) TRAINING
Simplified Explanation: The abstract describes a method of training a physical interface between a memory device and a memory controller using an autonomous sweep without the need for host commands.
Key Features and Innovation:
- Autonomous sweep for training the physical interface.
- Two-dimensional sweep for automatic parameter adjustments.
- Elimination of the need for host commands to trigger parameter sweeps.
Potential Applications: This technology can be applied in various memory devices and controllers to optimize performance and efficiency.
Problems Solved:
- Streamlining the training process for memory interfaces.
- Enhancing the communication between memory devices and controllers.
- Improving overall system performance.
Benefits:
- Increased efficiency in memory interface training.
- Enhanced data transfer speeds.
- Simplified operation without the need for constant host intervention.
Commercial Applications: Potential commercial applications include data centers, servers, and other computing systems where memory optimization is crucial for performance.
Prior Art: Researchers can explore existing patents related to memory interface training and autonomous parameter adjustments in memory devices.
Frequently Updated Research: Stay updated on advancements in memory interface technology, autonomous systems, and memory controller optimization for the latest developments in this field.
Questions about Memory Interface Training: 1. How does autonomous sweep training benefit memory device performance? 2. What are the potential drawbacks of using autonomous sweeps for memory interface training?
Original Abstract Submitted
training a physical interface between a memory device and a memory controller can be performed with an autonomous sweep. the sweep can occur without commands from the host to trigger each parameter sweep. with a two dimensional sweep, instead of interrupting a training mode for a first parameter to sweep a second parameter, circuitry in the memory can automatically sweep the second parameter in the training mode for the first parameter.
