Algorithmic partial analog-to-digital conversion in mixed-signal array processors
Bibliographic record
Abstract
We present an algorithmic analog-to-digital converter (ADC) architecture for large-scale parallel quantization of internally analog variables in externally digital array processors. The converter quantizes and accumulates a binary weighted sequence of partial binary-binary matrix-vector products computed on the analog array, under presentation of bit-serial inputs in descending binary order. The architecture combines algorithmic conversion of the residue, as in a standard algorithmic ADC, with synchronous accumulation of the partial products from the array. In conjunction with row-parallel digital storage of matrix elements in the array, two pipelined architectures are presented to accumulate partial products with common binary weight across rows: row-parallel ADC with digital post-accumulation, and row-cumulative ADC with analog pre-accumulation. Simulation results are presented to quantify the trade-off in precision and area for full-parallel flash, and row-parallel and row-cumulative partial algorithmic, analog-to-digital conversion on the array.
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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 | 0.001 |
| 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 | 0.000 |
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".