Hybrid bidirectional transceiver for multipoint‐to‐multipoint signalling across on‐chip global interconnects
Bibliographic record
Abstract
The authors propose a hybrid transceiver and an energy‐efficient link architecture for bidirectional multipoint‐to‐multipoint signalling across on‐chip global interconnect. The proposed link architecture eliminates the need for passive termination for bandwidth enhancement by means of active termination, reducing the required transmitter signalling current drastically and hence, improving the overall energy efficiency of the link. Also, compared to existing passive terminated interconnect with current‐mode receivers at all the points, the proposed link deploys low impedance current‐mode receivers providing active terminations at both the ends of the interconnect and high‐impedance voltage‐mode receivers, which do not consume any portion of the signalling current, at the intermediate nodes of the interconnect which further lowers the required transmitter signalling current. A mixed‐mode or hybrid transceiver architecture has been proposed for the aforementioned link, which can act either as a current‐mode transmitter or a current‐mode receiver or a voltage‐mode receiver. The architecture has been implemented in 0.18‐μm complementary metal oxide semiconductor technology for an interconnect of length 4 mm and having five transceiver nodes. The total energy efficiency of the architecture is 0.70 pJ/b for a speed of 3.0 Gb/s.
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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) | 0.002 | 0.001 |
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".