Memory Error-Handling Flow Reordering for Lower Recovery Latency
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Solution Overview
Problem
Conventional memory sub-systems employ static error-handling flows that fail to adapt to changing operating conditions, leading to inefficiencies such as increased latency and degraded performance due to the lack of consideration for latency measurements at a host or global level.
Innovation Solution
Implementing a system controller that adaptively adjusts error-handling operations using machine-learning models, optimizing error-handling flows based on real-time data and specific customer workloads, considering both latency measurements and aggregated statistical data to improve efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static error-handling flows are used in memory sub-systems, then device complexity is reduced and ease of operation is improved, but latency increases and performance degrades due to inability to adapt to changing operating conditions
Solution Approach 1:
The patent implements dynamic error-handling flows that automatically adjust based on real-time operating conditions. The system transitions from static, pre-defined error-handling sequences to dynamic sequences that are selected and modified based on current workload characteristics, error patterns, and system state, thereby reducing latency while maintaining adaptability
Solution Approach 2:
The system incorporates feedback mechanisms that monitor error-handling performance and use this information to optimize future error-handling flows. By analyzing latency measurements and error patterns from previous operations, the system learns and adapts its error-handling strategy, continuously improving performance without increasing device complexity
2Productivity
If machine-learning models are implemented for adaptive optimization, then latency and power consumption are reduced, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary optimization layer that sits between the host system and memory device. This intermediary component handles the complexity of machine-learning models and adaptive optimization algorithms, translating high-level performance goals into specific error-handling flow adjustments without requiring complex changes to the underlying memory device architecture
Solution Approach 2:
The system implements self-service capabilities where the error-handling optimization is performed autonomously based on observed patterns and performance metrics. The machine-learning models automatically adjust error-handling flows without requiring manual intervention or complex configuration, reducing the operational complexity despite the advanced algorithms employed
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 obtaining a reordered set of error-handling operations, wherein the reordered set adjusts an order of one or more error-handling operations of an ordered set of error-handling operations to be performed to recover data residing in a segment of the memory device based on latency data for previously-performed error-handling operations, wherein the latency data for the previously-performed error-handling operations depends on a workload of the segment of the memory device; and performing one or more error-handling operations of the reordered set of error-handling operations on the data residing in the segment of the memory device.