Memory Power-Off Time Tracking for Refresh Frequency Tuning
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Solution Overview
Problem
The challenge of managing data retention in memory systems is exacerbated by the need for frequent refresh operations, which reduce system performance and drain battery power when not connected to a stable power source, necessitating optimization of refresh frequency.
Innovation Solution
Implementing a tracking component to monitor power-off events and generate statistical information for analyzing time periods when devices are deployed in the field, allowing for optimized refresh operations based on collected data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If refresh operations are performed frequently to maintain data retention, then data reliability is improved, but system performance deteriorates and battery power is drained
Solution Approach 1:
The patent implements dynamic refresh rate adjustment by monitoring power event statistics (frequency and duration of power-offs) and adapting the refresh interval accordingly. When power events are frequent or prolonged, the refresh rate is reduced to conserve battery power while maintaining data integrity during actual operation. This dynamic adaptation resolves the contradiction by making the refresh operations flexible rather than fixed, allowing the system to optimize between reliability and performance based on actual usage patterns.
Solution Approach 2:
The system changes the parameter of refresh frequency based on collected power event data. By analyzing statistical information about power-off events over time, the system adjusts the refresh interval parameter to achieve optimal balance. This parameter change approach allows the system to maintain data retention reliability while minimizing performance impact and battery consumption by refreshing only when necessary.
2Reliability
If refresh operations are performed frequently to maintain data retention, then data reliability is improved, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts refresh operations based on power event monitoring. When the system detects frequent or prolonged power-off events, it reduces the refresh frequency to conserve battery power. This dynamic behavior allows the system to maintain data reliability during normal operation while minimizing energy consumption during periods when power availability is uncertain, directly resolving the contradiction between reliability and energy usage.
Solution Approach 2:
The system performs self-optimization by autonomously collecting power event statistics and using this information to determine appropriate refresh rates without external intervention. The memory subsystem monitors its own power conditions and adjusts its refresh behavior accordingly, enabling it to balance data retention requirements with battery conservation automatically.
3Reliability
If refresh operations are performed during idle periods to maintain data retention, then data reliability is improved, but time availability for memory requests decreases
Solution Approach 1:
The system performs preliminary monitoring of power events and uses this advance information to schedule refresh operations optimally. By collecting statistics about power-off patterns beforehand, the system can proactively adjust refresh timing to avoid conflicts with anticipated memory requests, thereby maintaining data reliability while minimizing impact on request handling time.
Solution Approach 2:
The refresh scheduling is made dynamic based on observed power event patterns. When the system detects that power-off events are frequent or prolonged, it reduces refresh frequency to free up more time for memory requests. This dynamic adjustment resolves the time conflict by adapting refresh operations to actual system needs rather than following a fixed schedule.
Data Source
AI summary
Time tracking for memory apparatuses is described herein. Time information (e.g., timestamps) received for each power event can be utilized to generate statistical information indicating the duration a computing device has spent in a particular power state. This statistical information can be retrieved by manufacturers for purposes such as data retention analysis.