Adaptive Memory Error-Handling Flows for Latency and Power
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Solution Overview
Problem
Conventional memory sub-systems employ static error-handling flows that do not adapt to changing operating conditions, leading to inefficiencies such as increased latency and degraded performance due to the failure to consider latency measurements and workload stress.
Innovation Solution
Implementing a system that automatically adjusts the order of error-handling operations using machine-learning models to optimize error-handling flows based on data collected from memory devices, considering specific workloads and environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static error-handling flows are used in memory sub-systems, then the system structure is simple and reliable, but latency increases and performance degrades due to inability to adapt to changing operating conditions
Solution Approach 1:
The patent applies dynamics by transitioning from static error-handling flows to dynamic, adaptive flows that automatically adjust based on real-time workload stress and environmental conditions. The system monitors metrics such as read errors, write amplification, and temperature to dynamically reorder error-handling operations, thereby reducing latency while maintaining reliability under varying operating conditions.
Solution Approach 2:
The patent implements feedback mechanisms where the memory sub-system continuously monitors performance metrics and uses this information to optimize error-handling flows. The system collects data on error rates, recovery times, and workload patterns, then feeds this information back into the error-handling decision logic to automatically adjust operations and minimize latency without compromising reliability.
2Productivity
If automated optimization using machine-learning models is implemented, then latency and power consumption are reduced, but device complexity increases
Solution Approach 1:
The patent applies self-service by enabling the memory sub-system to automatically optimize its own error-handling flows without external intervention. The system uses integrated machine-learning models that run locally within the memory sub-system, utilizing built-in monitors and collectors to gather performance data and autonomously adjust error-handling operations, thereby improving productivity while containing complexity within the system boundaries.
Solution Approach 2:
The patent utilizes parameter changes by adjusting key operational parameters such as error-handling operation order, recovery thresholds, and monitoring frequencies based on detected workload conditions. The machine-learning models analyze patterns in parameters like read error rates and temperature, then modify system behavior accordingly, achieving performance optimization through controlled parameter adjustment rather than fundamental system redesign.
3Loss of energy
If error-handling operations are dynamically reordered based on workload stress, then power consumption decreases, but measurement and detection difficulty increases
Solution Approach 1:
The patent introduces intermediary measurement components that translate complex workload stress into measurable parameters. The system uses monitors and collectors as intermediaries that track simplified metrics such as error rates, recovery times, and temperature, which serve as proxies for underlying workload stress. This approach enables energy-efficient dynamic reordering while avoiding the complexity of directly measuring and responding to multifaceted workload conditions.
Data Source
AI summary
Systems and methods are disclosed including a memory device and a processing device operatively coupled to the memory device. The processing device can perform operations including selecting sample data residing in the memory device; running a test on the sample data regarding a set of error-handling operations; and generating log data comprising a first order of the set of error-handling operations to be performed on data residing in a segment of the memory device.