Memory Error-Handling Flow Reordering for Lower 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 inability to consider latency measurements at a host or global level.
Innovation Solution
Implementing a system controller capable of adaptively adjusting error-handling operations using machine-learning models that optimize error-handling flows based on real-time data and workload patterns, dynamically reordering operations to minimize latency and improve performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
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 the sequence of error-handling operations based on real-time operating conditions, workload patterns, and historical performance data. This transforms the static, fixed sequence into a dynamic, adaptive sequence that optimizes latency performance while maintaining ease of operation through automated control.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor operating conditions, error rates, and latency measurements, then use this information to adjust the error-handling flow sequence. This closed-loop feedback enables the system to adapt to changing conditions and optimize performance without requiring manual intervention.
2Loss of time
If adaptive error-handling flows using machine-learning models are implemented, then latency is reduced and performance is improved, but device complexity increases
Solution Approach 1:
The patent introduces machine-learning models as intermediary components that bridge the gap between raw operating data and error-handling decisions. These models process complex patterns and recommendations, translating them into actionable flow sequence adjustments without requiring the core memory subsystem to become inherently more complex.
Solution Approach 2:
The system implements self-service capabilities where the error-handling flow automatically optimizes itself using embedded machine-learning models and real-time data. This self-optimizing behavior reduces the need for external configuration and management, offsetting the added complexity through automated self-management.
3Loss of energy
If conventional static error-handling flows are used, then power consumption is higher and device lifespan is reduced, but the system remains simpler to implement
Solution Approach 1:
The patent implements dynamic error-handling flows that adapt the sequence of operations based on real-time conditions, enabling the system to minimize power-consuming operations when possible and extend device lifespan through optimized access patterns. This dynamic adaptation reduces unnecessary power consumption and wear without requiring complex manual configuration.
4Adaptability or versatility
If static error-handling flows are employed, then the system is easier to implement and maintain, but it cannot adapt to changing operating conditions leading to degraded performance
Solution Approach 1:
The system incorporates feedback mechanisms that continuously monitor operating conditions, workload patterns, and performance metrics, then use this information to dynamically adjust the error-handling flow sequence. This feedback-driven adaptation enables the system to respond to changing conditions automatically, achieving high adaptability through automated control loops.
Solution Approach 2:
The error-handling system implements self-service capabilities where machine-learning models and real-time data enable automatic optimization of the flow sequence. This self-optimizing behavior provides adaptability to changing conditions without requiring external intervention or complex manual configuration, as the system manages its own optimization autonomously.
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 sending, to a device that provides error-handling flow optimization, an ordered set of error-handling operations to be performed to recover data residing in a segment of the memory device; receiving, from the device that provides the error-handling flow optimization, a reordered set of error-handling operations, wherein the reordered set adjusts an order of one or more error-handling operations of the ordered set of error-handling operations, wherein the reordered set is obtained by applying the ordered set of error-handling operations to a trained machine learning model, wherein the trained machine learning model is based on latency data for previously-performed error-handling operations, and 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 to the data residing in the segment of the memory device.