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 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 | 0.001 |
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 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".