Skew compensation in energy recovery clock distribution networks
Bibliographic record
Abstract
In this study a new approach for skew compensation in energy recovery clock distribution networks is introduced by manipulating the operating speed of the flip-flops. The STMicroelectronics 90 nm technology allows the use of devices with different threshold voltages, namely: high threshold voltage (HVT), standard threshold voltage (SVT) and low threshold voltage (LVT). Three types of flip-flops of equal input load: ‘fast’, ‘standard’ and ‘slow’ are used. Timing parameters of the flip-flops are adjusted by manipulating the switching threshold of the clock port of the flip-flops. A fast/slow flip-flop has a shorter/longer TDQ delay, compared with a standard flip-flop for the same setup time (TDCLK). Distributing flip-flops according to their delay requirements would reduce the effect of the clock's skew on the outputs of sequentially adjacent flip-flops. Owing to the slow rise time of the sinusoidal clock signal used in energy recovery clock distribution networks compared to the conventional square-wave clock, the skew that can be compensated for in energy recovery clock distribution networks using this approach would be much higher than in square-wave clock distribution networks. This approach increases the skew bounds required by algorithms to balance the skew in the clock tree leading to reduced design complexity. Theoretical analysis and simulation results using STMicroelectronics 90 nm technology at a clock frequency of 500 MHz show that this approach is feasible and effective where a skew of up to 6.2% of the clock period can be compensated for in the example used. In addition, constructing clock trees using the skew slack provided in the proposed technique in a new modified differed merge embedding (DME) algorithm on five benchmarks have shown that the proposed technique enables an average reduction of 11.5% in total wire length and 53.2% reduction in the number of wire elongations. Balancing the skew in the clock tree using buffers was not considered here since inserting a buffer in the clock's path eliminates the energy recovery property. As an example of illustrating the proposed methodology, the authors have used the Elmore delay model with a selected energy recovery flip-flop to verify the practicality of the proposed scheme. Better results can be obtained by using different flip-flops. The method can generally be applied to energy recovery or square-wave clocking if different flip-flops of various speeds are used.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".