Autonomous DRAM Training Sweep for Boot Time Reduction
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Solution Overview
Problem
The inefficiency of existing memory subsystems in training I/O interfaces due to the need for multiple commands to change parameter settings during the training process, which increases system boot time, especially when sweeping multiple parameters.
Innovation Solution
Implementing an autonomous sweep mechanism within the memory device to automatically adjust configuration parameters without the need for explicit commands from the host, using in-built logic and firmware to monitor transitions and perform sweeps internally, thereby reducing the reliance on MRW or MPC commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system sends MRW or MPC commands to trigger training mode for each parameter sweep, then the training can be performed with proper margins, but the training time and system boot time increase significantly
Solution Approach 1:
The memory device autonomously performs parameter sweeps and training operations without requiring continuous host intervention. The device self-manages the training process by automatically transitioning between training modes and adjusting parameters based on host-initiated commands, eliminating the need for the host to send individual MRW or MPC commands for each parameter change.
Solution Approach 2:
The system performs preliminary configuration by having the host send a single command to initiate training mode, and the memory device pre-prepares to automatically execute the full parameter sweep sequence. This preliminary action setup allows the subsequent training to proceed without repeated command interruptions, reducing overall training time while maintaining proper margin validation.
2Adaptability or versatility
If the system interrupts training mode to change parameter values for each sweep, then all parameters can be optimized, but the number of commands increases and efficiency decreases
Solution Approach 1:
The training process maintains continuity by allowing the memory device to remain in training mode throughout the entire parameter sweep sequence. Instead of repeatedly exiting and re-entering training mode with separate commands, the device continuously sweeps through parameters (such as Vref and delay) within a single sustained training session, maximizing productivity while still achieving full parameter optimization.
Solution Approach 2:
Multiple parameter sweeps that previously required separate command sequences are merged into a single continuous training operation. The memory device combines Vref sweeps, delay sweeps, and other parameter adjustments into one integrated training mode execution, reducing the total number of commands required while maintaining the ability to optimize all parameters.
3Measurement precision
If multiple parameters are swept with individual commands for each setting, then comprehensive training is achieved, but the system boot time increases
Solution Approach 1:
The memory device autonomously executes comprehensive parameter sweeps and signal performance measurements without requiring the host to send individual commands for each parameter setting. The device self-manages the entire sequence of training operations, maintaining precise measurement capabilities while significantly reducing the time required during system boot.
Solution Approach 2:
The system performs preliminary setup by having the host initiate training mode with a single command, and the memory device pre-prepares to automatically execute all necessary parameter sweeps and measurements. This preliminary action allows comprehensive signal performance evaluation to proceed without repeated command interruptions, reducing boot time while maintaining measurement precision.
Data Source
AI summary
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.