Cost-effective lifetime and yield optimization for NoC-based MPSoCs
Bibliographic record
Abstract
As manufacturing processes scale, designers are increasingly dependent on techniques to mitigate manufacturing defect and permanent failure. In embedded systems-on-chip, system lifetime and yield can be increased using slack —under-utilization in execution and storage resources—so that when components are defective, data and tasks can be remapped and rescheduled. For any given system, the design space of possible slack allocations is both large and complex, consisting of every possible way to replace each component in the initial system with another from the component library. Based on the observation that useful slack is often quantized, we have developed Critical Quantity Slack Allocation (CQSA), an approach that effectively and efficiently allocates execution and storage slack to jointly optimize system yield and cost. While exploring less than 1.4% of the slack allocation design space, our approach consistently outperforms alternative slack allocation techniques to find sets of designs within 1.4% of the lifetime-cost Pareto-optimal front. When applied to yield-cost optimization, our approach again outperforms alternative techniques, exploring less than 1.62% of the design space to find sets of designs within 4.27% of the yield-cost Pareto-optimal front. One advantage of managing failure at the system level is that the same techniques that improve lifetime often also improve yield. As a result, with little modification, CQSA is further able to perform effective joint optimization of lifetime and yield, finding designs within 1.6% of the Pareto-optimal front.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".