Bibliographic record
Abstract
Multi-ported RAMs are essential for high-performance parallel computation systems. VLIW and vector processors, CGRAs, DSPs, CMPs and other processing systems often rely upon multi-ported memories for parallel access, hence higher performance. Although memories with a large number of read and write ports are important, their high implementation cost means they are used sparingly in designs. As a result, FPGA vendors only provide dual-ported block RAMs to handle the majority of usage patterns. In this paper, a novel and modular approach is proposed to construct multi-ported memories out of basic dual-ported RAM blocks. Like other multi-ported RAM designs, each write port uses a different RAM bank and each read port uses bank replication. The main contribution of this work is an optimization that merges the previous live-value-table (LVT) and XOR approaches into a common design that uses a generalized, simpler structure we call an invalidation-based live-value-table (I-LVT). Like a regular LVT, the I-LVT determines the correct bank to read from, but it differs in how updates to the table are made; the LVT approach requires multiple write ports, often leading to an area-intensive register-based implementation, while the XOR approach uses wider memories to accommodate the XOR-ed data and suffers from lower clock speeds. Two specific I-LVT implementations are proposed and evaluated, binary and one-hot coding. The I-LVT approach is especially suitable for larger multi-ported RAMs because the table is implemented only in SRAM cells. The I-LVT method gives higher performance while occupying less block RAMs than earlier approaches: for several configurations, the suggested method reduces the block RAM usage by over 44% and improves clock speed by over 76%. To assist others, we are releasing our fully parameterized Verilog implementation as an open source hardware library. The library has been extensively tested using ModelSim and Altera's Quartus tools.
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) | 0.000 | 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 | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".