Stochastic TDC Using Common-Mode Time Dithering and Passive Approximate Adders
Bibliographic record
Abstract
The stochastic time-to-digital converter (STDC) presents a novel approach to automating the design and implementation process, delivering high performance with strong resilience to process variations and layout-induced artifacts, although with increased silicon area and higher power consumption. To effectively lower these costs, this article presents a 10-bit fully synthesizable STDC design using a removal-free common-mode time dithering technique, which significantly reduces the numbers of delay cells and D-type flip-flops (DFFs) required for requisite levels of stochastic operation. This also reduces the size of the associated backend unary-to-binary (U2B) encoder. In addition, passive approximate adders are used to further reduce the area of the U2B for a compact design and significantly lower time for digital place and route. Two STDC prototypes are implemented in a 12-nm FinFET process with a conventional adder and passive approximate adder, respectively. STDC prototypes achieve energy efficiency of 160 dB, while the one using passive approximation adder improves the area efficiency from 28.6 to$19.1~{\mu \text {m}^{2}}$/step.
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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 | 0.000 |
| 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 | 0.001 | 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".