Bayesian‐Optimization‐Based Post‐Silicon Offset‐Cancelation Technique for Analog Multipliers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents a software‐controlled offset‐cancelation technique for analog multipliers that relies on the Bayesian‐optimization algorithm. The capability of the technique was investigated on a test multiplier, which was developed for future use in machine learning (ML) accelerators whose convergence is sensitive to process‐variation‐induced DC offsets. By adjusting the multiplier biasing voltages, the proposed Bayesian‐optimization‐based method was able to reduce the offset within ±1.8 mV from an uncorrected maximum offset of 10.6 mV. In addition to reducing offsets, the measurements of the 65‐nm CMOS multiplier also showed an average linearity‐error improvement of nearly 10%, from 12.2% prior to offset correction to 2.2% after correction. We demonstrate that the proposed offset correction improved the learning outcome accuracy for MNIST dataset digit classification from approximately 10% to 90%.
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.
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 | 0.001 | 0.001 |
| 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