Multi-ported memories for FPGAs via XOR
Bibliographic record
Abstract
Multi-ported memories are challenging to implement with FPGAs since the block RAMs included in the fabric typically have only two ports. Any design that requires a memory with more than two ports must therefore be built out of logic elements or by combining multiple block RAMs. The recently-proposed Live Value Table (LVT) design provides a significant operating frequency improvement over conventional approaches. In this paper we present an alternative approach based on the XOR operation that provides multi-ported memories that use far less logic but more block RAMs than LVT designs, and are often smaller and faster for memories that are more than 512 entries deep. We show that (i) both designs can exploit multipumping to trade speed for area savings, (ii) that multipumped XOR designs are significantly smaller but moderately slower than their LVT counterparts, and (iii) that both the LVT and XOR approaches are valuable and useful in different situations, depending on the constraints and resource utilization of the enclosing design.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".