Mapping Arbitrary Logic Functions into Synchronous Embedded Memories For Area Reduction on FPGAs
Bibliographic record
Abstract
This work describes a new mapping technique, RAM-MAP, that identifies parts of circuits that can be efficiently mapped into the synchronous embedded memories found on field programmable gate arrays (FPGAs). Previous techniques developed for mapping into asynchronous embedded memories cannot be used because modern FPGAs do not have asynchronous embedded memories. After technology mapping, an area-prediction cost function is used to guide the selection of logic cones to be placed in embedded memories. Extra logic is added to compensate for missing asynchronous functionality on the synchronous memories. Experiments conducted on Altera's Stratix device family indicate that this embedded memory mapping technique can provide an average area reduction of 6.2% and up to 32.5% on a large set of industrial designs. A small architecture change that increases the size of the FPGA fabric by 0.05% can increase the average area reduction to 14.1% and up to 59.1% on the same design set
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".