On the Fault Tolerance of Stochastic Decoders
Bibliographic record
Abstract
This paper investigates the capability of iterative decoders based on stochastic computing (stochastic decoders) to tolerate circuit soft errors while maintaining good bit error rate performance and low error floors in the context of low-density parity-check (LDPC) coding. Soft errors can be intended faults as a result of either VDD scaling to reduce power consumption or overclocking the system to achieve a higher throughput. They can also be unintended faults as a result of temperature or process variations. We developed two models to emulate these circuit errors at the system level. We apply our models to two standardized LDPC codes (10GBASE-T and WiMAX). Simulation results show that stochastic decoding is very tolerant to faults and errors. Hence, stochastic decoding can be very useful in systems with very low power or high performance requirements where we can push the limits of power or speed by lowering VDD or highly overclocking the system while maintaining good performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".