Improving Performance under Process and Voltage Variations in Near-Threshold Computing Using 3D ICs
Bibliographic record
Abstract
Near-threshold computing (NTC) circuits have been shown to offer significant energy efficiency and power benefits but with a huge performance penalty. This performance loss exacerbates if process and voltage variations are considered. In this article, we demonstrate that three-dimensional (3D) IC technology can overcome this limitation. We present a detailed case study with a 28nm commercial-grade core at 0.6V operation optimized with various 3D IC physical design methods. First, our study under the deterministic case shows that 3D IC NTC design outperforms 2D IC NTC by 29.5% in terms of performance at comparable energy. This is significantly higher than the 12.8% performance benefit of 3D IC at nominal voltage supplies due to higher delay sensitivity to input slew at lower voltages. Second, it is well demonstrated that transistor delay is more sensitive to voltage changes at NTC operation. However, our full-chip study reveals that IR drop effect on 2D/3D IC NTC performance is not severe due to the low power consumption and hence lower IR drop values. Third, die-to-die variation impact on full-chip performance is visible in 3D IC NTC designs, but it is not worse compared to 2D IC NTC designs. This is mainly due to the shorter critical path length in 3D IC NTC designs.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 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".