Bridging the gap between soft and hard eFPGA design
Bibliographic record
Abstract
Potential cost savings that come from the ability to make post fabrication changes in System-on-Chip (SoC) designs make embeddable Field Programmable Gate Array (eFPGA) cores an attractive design option. However, they are only available as "hard" macros from vendors as a small number of fixed size cores, and may not be optimal in terms of area, power or delay for a given SoC. A "soft" eFPGA methodology [01] [02] based on the ASIC design flow was used to create small amounts of programmable logic but incurs significant overhead. In this thesis, it is shown that this overhead can be reduced by deploying architecture-specific tactical standard cells in the ASIC flow, making eFPGA generation configurable, and imposing a regular structure on eFPGA architectures. For the set of benchmarks considered, the use of tactical standard cells resulted in area and delay savings of 58% and 40% respectively, when compared to cores implemented with generic standard cells [02]. Also, a proposed IP-generator-based approach for eFPGA design is shown to achieve results that are competitive with commercial full-custom hard eFPGA cores. For example, for some large benchmark circuits (over 1000 4-LUTs) the generated eFPGA fabrics were up to 40% smaller than available hard eFPGA cores. Finally, it is shown that a regular structured architecture makes it possible to generate fabrics with logic capacities that gready exceed what was previously possible [02] [15]. In addition, a structured layout approach yielded a 36% reduction (average) in wire lengths.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".