Cache Coherence Scheme for HCS-Based CMP and Its System Reliability Analysis
Bibliographic record
Abstract
In previous work, a new network switch architecture, hybrid circuit-switched (HCS) network, has been proposed and evaluated. In doing so, it has been studied for use in a multi-processor system, with a focus on power and throughput. However, cache coherence and its connection with chip reliability have not been fully studied previously for multi-processor systems. In this paper, we study this problem by discussing the implementation of cache coherence on a HCS-based chip multi-processor and present a way to model the reliability of these protocols based on fault tree analysis and two-terminal networking models. We focus our efforts on three cache coherence protocols: Write-Once, Modified, Exclusive, Shared, Invalid (MESI), and Modified, Owned, Exclusive, Shared, Invalid (MOESI), and obtain expressions for the reliability probabilities of the system. Our results show that the Write-Once protocol is 14% less reliable than MOESI, while the MESI protocol is 2.5% less reliable than MOESI. We also demonstrate that the reliability of these protocols are 40.22% and 59.83% better, on average, when implemented on an HCS network rather than an elastic buffer-based network or a bus-based network, respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".