Multi-Decoder Error Correction Scheduling for Latency and Throughput
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Solution Overview
Problem
Existing error correction systems with multiple decoders face challenges in optimizing decoding latency and throughput, particularly due to queuing of codewords and inefficient distribution between low latency and high throughput decoders and high error correction capability decoders.
Innovation Solution
Implementing a logic that intelligently distributes codewords based on checksums, estimated decoding iterations, and accumulated numbers to optimize the use of decoders, ensuring that codewords are decoded by the most effective decoder, either the low latency and high throughput decoder or the high error correction capability decoder, depending on the noise level and queue status.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple decoders are used to improve decoding throughput, then decoding throughput is improved, but decoding latency increases due to queuing and distribution overhead
Solution Approach 1:
The system performs preliminary actions by computing checksums and estimating the number of decoding iterations needed before the actual decoding process. This allows the scheduler to pre-determine which decoder should handle each codeword, avoiding runtime decision-making delays and optimizing the distribution of codewords to appropriate decoders in advance.
Solution Approach 2:
The system dynamically adjusts the distribution of codewords to different decoders based on real-time conditions such as queue status, noise levels, and estimated decoding complexity. The scheduler can adaptively route codewords to either low-latency decoders or high-throughput decoders depending on current system state, making the system flexible and responsive to changing conditions.
2Loss of time
If codewords are distributed to low latency decoders, then decoding latency is reduced, but error correction capability deteriorates for high noise level codewords
Solution Approach 1:
The system applies local quality by assigning different types of decoders to different codewords based on their specific characteristics. Low-latency decoders are used for codewords with low noise levels, while high-error-correction-capability decoders are used for codewords with high noise levels. This localized optimization ensures that each codeword receives the most appropriate decoding treatment for its specific condition.
Solution Approach 2:
The system changes the parameter of decoder selection based on the noise level and error characteristics of each codeword. By evaluating the checksum and estimated iteration count, the system dynamically adjusts which decoder type (low-latency or high-correction) should process each codeword, optimizing the balance between speed and reliability for each individual decoding task.
3Reliability
If codewords are distributed to high error correction capability decoders, then error correction capability is improved, but decoding throughput decreases due to longer processing time
Solution Approach 1:
The system applies partial action by using high-error-correction-capability decoders only for the portion of codewords that actually require extensive error correction (those with high noise levels). The majority of codewords with low noise levels are processed by low-latency decoders, avoiding the excessive processing time of high-capability decoders for simple cases while still maintaining the ability to handle difficult codewords when necessary.
4Productivity
If intelligent distribution logic is implemented, then decoding efficiency is improved, but device complexity increases
Solution Approach 1:
The system implements self-service by having each codeword essentially select its own decoder through the checksum and iteration estimation process. The codeword's characteristics (noise level, error pattern) automatically determine which decoder type should process it, reducing the need for complex centralized control logic while still achieving intelligent distribution based on actual decoding requirements.
Data Source
AI summary
Techniques are described for improving the decoding latency and throughput of an error correction system that includes multiple decoders. For example, the error correction system intelligently distributes and balances the decoding of codewords between the different decoders. In particular, the error correction system can consider different factors associated with decoding various codewords including, for instance, the checksum of a codeword that is to be decoded, an estimated number of decoding iterations to decode the codeword by a decoder based on the checksum, and/or an accumulated number of decoding iterations for decoding by the decoder of the codeword in addition to other codewords already buffered for the decoder. Given these factors, the error correction system can generate a decision to decode the codeword by the decoder or by another decoder of the error correction system, where the decision optimizes the performance.