Architectures and algorithms for synthesizable embedded programmable logic cores
Bibliographic record
Abstract
As integrated circuits become more and more complex, the ability to make post-fabrication changes will become more and more attractive. This ability can be realized using programmable logic cores. Currently, such cores are available from vendors in the form of a "hard" layout. In this paper, we focus on an alternative approach: vendors supply a synthesizable version of their programmable logic core (a "soft" core) and the integrated circuit designer synthesizes the programmable logic fabric using standard cells. Although this technique suffers increased speed, density, and power overhead, the task of integrating such cores is far easier than the task of integrating "hard" cores into an ASIC. For very small amounts of logic, this ease of use may be more important than the increased overhead. This paper presents two synthesizable programmable logic core architectures, describes the associated place and route CAD tools, and compares the two architectures to each other, and to a "hard" programmable logic core. It also shows how these cores can be made more efficient by creating a non-rectangular architecture, an option not available to "hard" core vendors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.006 | 0.002 |
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".