Scalable Synthesis and Clustering Techniques Using Decision Diagrams
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
Binary-decision diagrams (BDDs) have proven to be an efficient means to represent and manipulate Boolean formulas and sets due to their compactness and canonicity. In this paper, we leverage the efficiency of BDDs for new areas in field-programmable gate-array (FPGA) computer-aided design (CAD) flow including cut generation and clustering by reducing these problems to BDDs and solving them using Boolean operations. As a result, we show that this leads to more than 10 reduction in runtime and memory use when compared to previous techniques, as reported by Mishchenko and Lin. This speedup allows us to apply this paper to new areas in the FPGA CAD flow previously not possible. Specifically, we introduce a new method to solve the logic-synthesis elimination problem found in FBDD, a reported BDD synthesis engine with an order-of-magnitude speedup over SIS. Our new elimination algorithm results in an overall speedup of 6 in FBDD with no impact on circuit area.
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) | 0.001 | 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