A Multi-ported Memory Compiler Utilizing True Dual-Port BRAMs
Bibliographic record
Abstract
Recent work has shown how multi-ported RAMs can be built out of dual-ported RAMs. Such techniques combine two structures: a set of "data banks" to hold the data, and a method for selecting the bank containing the last-written data, often called a live-value table (LVT). Most previous work has focused on the design of the LVT to reduce area and improve performance. In this paper, we instead reduce area by optimizing the design of the "data banks" portion. The optimization is embedded into a memory compiler that solves a set cover problem. When the set cover problem is solved optimally, the data banks use minimum area. Our technique applies to multi-ported RAMs that have a structural pattern we describe as "switched ports". Switched ports are a generalization of true ports, where a certain number of write ports can be dynamically switched into a possibly different number of read ports using one common read/write control signal. Furthermore, a given application may have multiple sets, each set with a different read/write control. While previous work generates multi-port RAM solutions that contain only true ports, or only simple ports, we contend that using only these two models is too limiting and prevents optimizations from being applied. Experimental results on 10 random instances of multi-port RAMs show 17% BRAM reduction on average compared to the best of other approaches. The compiler and a fully parameterized Verilog implementation is released as an open source library. The library has been extensively tested using Altera's EDA tools.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".