Design of Light-Weight Timing Error Detection and Correction Circuits for Energy-Efficient Near-Threshold Voltage Operation
Bibliographic record
Abstract
Near-threshold voltage (NTV) operation has the potential to improve the energy efficiency of digital integrated circuits. However, the use of a conservative timing guard band to avoid the timing errors introduces excessive timing margins, thus causing larger energy dissipation in the NTV region. An error-tolerant design based on timing error detection and correction circuits has been shown to be a promising solution to mitigate these issues. This paper presents a light-weight timing error-tolerant flip-flop (ETFF) design. This design detects timing errors using a node transition signal detector with only nine transistors and corrects these errors during the same clock cycle. Moreover, transistor sizing is explored to optimize the trade-off between performance and area overhead. The proposed ETFFs are inserted into a monitored circuit by replacing original flip-flops at timing-monitored points. To further reduce the overhead, we develop a mean-time-to-failure-aware method to select the monitored points by simultaneously considering the critical path coverage and activation rates of flip-flops. The simulation results show that a CNN accelerator using the proposed timing error-tolerant design implemented in the SMIC CMOS 40 nm process can robustly work at 1.1–0.3 V with only 3.5% area overhead. Furthermore, this design reduces the area overhead by 54.68% and improves the energy efficiency by 53.69% at 0.6 V, compared with the Razor flip-flop design. The advantage of the proposed design lies in that it requires smaller circuit overheads and can work reliably in a wider range of supply voltages.
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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 | 0.000 | 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".