Differential signaling with a reduced number of signal paths
Bibliographic record
Abstract
Differential signaling is often used for digital chip-to-chip interconnects because it provides common-mode noise rejection. Unfortunately, differential signals generally require 2N signal paths to communicate N signals. In this paper, a method for differential signaling is described that requires as few as N+1 signal paths for N signals. Using this method, the signal values appear incrementally between neighboring matched signal paths. The technique, called incremental signaling, is similar to dicode (1-D) partial response signaling except that the sequence is transmitted in parallel over a bus of wires rather than sequentially in time. Theoretical and simulated bit error rates are presented for several possible implementations of an encoder/transmitter and receiver/decoder for a digital data bus including peak detection and maximum likelihood sequence detection (MLSD). Peak detection uses N+1 signal paths and results in a 3-dB performance degradation with respect to independent noise compared with fully differential signaling. The Viterbi algorithm for MLSD uses N+2 signal paths but provides only a 1.25 dB improvement over peak detection due to correlated noise on the (1-D)-coded sequence. Modified Viterbi algorithms that use N+2 signal paths are introduced to cancel the correlated noise sources, resulting in a bit error rate performance comparable with fully differential signaling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".