A dynamic address decode circuit for implementing range addressable look-up tables
Why this work is in the frame
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Bibliographic record
Abstract
A Range Addressable Look Up Table (RALUT) is a non-linear memory storage element that has been shown to significantly reduce hardware requirements for matching data in particular applications. However, its ability to perform parallel pattern matching on large words can be applied in many areas. Most of the RALUT circuits presented in literature thus far are built with logic gates and tri-state buffers so that they are easily synthesizable and implemented with other components of the overall design. These circuits are not competitive with modern memory in terms of area, timing, power and functionality. The only significant difference between a RALUT and a standard LUT is the address decoding system. In this paper, we will show a preliminary dynamic address decode circuit which can be used to build a scalable full custom read-only RALUT implementations. We will show significant reductions in area, timing and power compared to a previously published synthesized version.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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