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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$19.1~{\mu \text {m}^{2}}$ </tex-math></inline-formula>/step.
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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 | 0.001 |
| 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".