Hierarchical ECC Decoding for Low-Latency Memory Error Correction
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Solution Overview
Problem
Current error correction codes (ECCs) in memory systems face challenges in balancing low latency with high correction capability, particularly in emerging memory technologies like phase-change memory, magnetoresistive random access memory, and 3D XPoint memory, where storage applications can tolerate variable access latencies.
Innovation Solution
The implementation of a hierarchical decoding scheme using fast decoding (FD) and accurate decoding (AD) decoders, where FD decoders correct errors quickly with low latency and flag failures, while AD decoders handle more complex corrections when needed, utilizing ECC concatenation and multiple decoders to maintain low latency and high correction capability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single powerful ECC decoder is used to achieve high correction capability, then error correction reliability is improved, but access latency increases
Solution Approach 1:
The ECC decoding function is segmented into two distinct decoders: a fast decoder (FD) for quick error correction and an accurate decoder (AD) for comprehensive correction. The FD handles common error cases rapidly, while the AD processes more complex errors, thereby reducing overall access latency while maintaining high correction capability.
Solution Approach 2:
The system dynamically selects which decoder to use based on the error characteristics of the received data. The controller determines whether to invoke the FD or AD based on syndrome analysis, allowing the system to adapt its correction strategy to match the actual error conditions, optimizing both speed and reliability.
2Speed
If multiple decoders are used to reduce latency, then access speed is improved, but device complexity increases
Solution Approach 1:
The decoder is segmented into specialized components: the FD uses a simplified syndrome calculation and correction logic for fast operation, while the AD implements a more comprehensive correction algorithm. This segmentation allows each component to be optimized independently, managing overall system complexity while achieving high speed.
Solution Approach 2:
The controller acts as an intermediary that manages the interaction between the FD and AD. It receives data from memory, performs initial syndrome analysis, and directs the appropriate decoder based on error conditions, thereby coordinating the complex multi-decoder system without requiring direct complex interactions between the decoders themselves.
3Speed
If fast decoding is used to reduce latency, then access speed is improved, but correction capability for complex errors deteriorates
Solution Approach 1:
The system dynamically switches between the FD and AD based on the complexity and type of errors detected. The syndrome analysis performed by the controller determines whether errors are correctable by the faster FD or require the more powerful AD, ensuring that correction capability matches the actual error conditions while maintaining optimal speed.
Solution Approach 2:
The controller performs preliminary syndrome analysis before invoking a decoder, identifying error patterns that can be handled by the FD versus those requiring the AD. This preliminary assessment ensures that the appropriate decoder is selected in advance, preventing complex errors from being mishandled by the faster but less capable FD.
Data Source
AI summary
Apparatuses and methods related to correcting errors can include using FD decoders and AD decoders. Correcting errors can include receiving input data from the memory array, performing a plurality of operations associated with an error detection on the input data, and providing, based on processing the input data, output data, a validation flag, and a plurality of parity bits to a second decoder hosted by a controller coupled to the memory device.