Enhancement of incremental design for FPGAs using circuit similarity
Bibliographic record
Abstract
This paper presents an efficient algorithm to detect the global topological similarity between two circuits. By applying the proposed circuit similarity algorithm in an incremental design flow, IDUCS (incremental design using circuit similarity), the design and optimization effort in the previous design iterations is automatically captured and can be used to guide the next design iteration. IDUCS is able to identify the similarity between the original netlist and the modified one with aggressive resynthesis, which might destroy the naming and local structures of the original netlist. This is superior to the existing design preservation approaches such as naming and local topological matching. Furthermore, IDUCS simply inserts a plugin for circuit similarity detection, and therefore preserves the “push-button” feature, significantly simplifying the engineering complexity of incremental tasks. As a case study, we perform the proposed IDUCS process to generate the placement for a logically resynthesized netlist based on the placement of the original netlist and the circuit similarity between the original and the modified logic-level netlists. The experimental results show our IDUCS-based placement is 28X faster than versatile place and route (VPR) with comparable wire length and estimated critical delay.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".