Bibliographic record
Abstract
Modern FPGAs now contain a selection of "hard" digital structures such as memory blocks and multipliers (Altera, 2003, Xilinx, 2003, QuickLogic, 2003, Actel, Lattice, 2004) in addition to the usual "soft" programmable logic typically consisting of lookup tables (LUTs) and flip-flops. These hard structures are a major benefit (in area and speed) for those applications that need them, but are completely wasted if an application circuit docs not require them. Finding other ways to use these structures will benefit these applications. In this paper, the authors presented a technique to map multiplexers to unused hard multipliers on an FPGA. An RTL synthesis tool flow that implements this technique over a set of benchmarks was created. While some circuits see no reduction in LUT count at all, others show meaningful improvements ranging from 10% to 70%. On average across the whole set of circuits the technique achieves a 7.3% reduction on the number of LUTs used. In some cases, however, the operating frequency of the circuit is reduced significantly.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.007 | 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 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".