Adaptive Decoding for Solid-State Memory Read Latency
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Solution Overview
Problem
Current storage systems face challenges in reducing read latency and error rates when retrieving data from solid-state storage, often due to overly complex decoding schemes that are not optimized for specific read operations.
Innovation Solution
The use of program/erase (P/E) cycle values, retention times, and page numbers to dynamically determine appropriate decoding parameters for each read request, allowing for more error-free data retrieval and reduced latency by adapting decoding schemes based on these variables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complex decoding schemes are used to retrieve data from solid-state storage, then error correction capability is improved, but read latency increases
Solution Approach 1:
The patent applies dynamics by making the decoding scheme adaptable and variable based on runtime conditions. Specifically, the system dynamically selects between different decoding approaches (full decoding vs. fast path) based on factors such as program/erase cycle counts, retention time, and error indicators. This allows the system to optimize read latency by using simpler decoding when conditions permit while maintaining error correction capability when needed.
Solution Approach 2:
The patent changes operational parameters to resolve the contradiction. It monitors parameters such as program/erase cycle counts, retention time, and read disturbance indicators, then adjusts the decoding strategy accordingly. By changing the decoding parameter (from full decoding to fast path or vice versa) based on these monitored parameters, the system achieves both low latency and high reliability.
2Reliability
If complex decoding schemes are used for all read operations, then error-free data retrieval is improved, but system performance decreases
Solution Approach 1:
The patent segments the read operation population into different categories based on their error risk profiles. It divides reads into those that require full decoding (high-risk reads with many P/E cycles or long retention times) and those that can use fast path decoding (low-risk reads). This segmentation allows the system to apply appropriate decoding complexity to each segment, improving overall performance while maintaining error-free retrieval for critical reads.
Solution Approach 2:
The patent applies partial action by implementing a fast path decoding mechanism that performs only necessary error checking for reads that are likely to be error-free. Instead of always performing complete decoding, the system applies partial decoding (fast path) when conditions indicate low error risk, and full decoding when conditions indicate high error risk. This partial action approach maintains productivity while ensuring error-free retrieval when needed.
3Ease of operation
If standard read operations are used without optimization, then operational simplicity is maintained, but read performance and reliability are suboptimal
Solution Approach 1:
The patent implements self-service by enabling the storage system to automatically monitor its own health parameters (P/E cycle counts, retention time, error indicators) and autonomously select the appropriate decoding strategy without external intervention. The system serves itself by making real-time decisions about decoding complexity based on its own operational state, thereby maintaining ease of operation while optimizing read performance and reliability.
Data Source
AI summary
In general, embodiments of the technology relate to improving read performance of solid-state storage by using decoding schemes deemed particularly suitable for the read operation that is currently being performed. More specifically, embodiments of the technology relate to using program/erase (P/E) cycle values, retention times, and page numbers in order to determine the appropriate decoding parameters to use when reading data that has been previously stored in the solid-state storage.