Bibliographic record
Abstract
As FPGAs push ever deeper into mainstream digital design, there is an increasing desire for high-performance circuits. This paper describes a manual editor, called EVE, which can assist a designer to perform manual packing, placement and pipelining of commercial FPGA circuits to achieve a meaningful increase in performance. This effort is inspired by Von Herzen's paper [15] [16], which proposed the notion of an "Event Horizon" - a high-speed circuit design approach in which complete knowledge of the timing effect of every synthesis change is used. It is very laborious to implement circuits using this approach; therefore we try to augment manual design tools in order to make this Event Horizon methodology easier to perform. This paper describes a first step in that direction, which focuses on placement, packing and pipelining. EVE provides an interactive environment that immediately reroutes and timing analyzes after each user circuit modification, giving an exact value for critical path delay. It can also suggest good placement positions and provide flip-flop insertion assist during pipelining. Compared to a state-of-the-art Synthesis and place and route flow, we used EVE to achieve an average of 12.7% higher operating frequency on a set of eight Xilinx Virtex-E circuits of 250 or fewer LUTs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.318 | 0.173 |
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".