Differential signaling with a reduced number of signal paths
Why this work is in the frame
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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.
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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 | 0.000 |
| 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 it