Area-efficient FPGA logic elements: Architecture and synthesis
Bibliographic record
Abstract
We consider architecture and synthesis techniques for FPGA logic elements (function generators) and show that the LUT-based logic elements in modern commercial FPGAs are over-engineered. Circuits mapped into traditional LUT-based logic elements have speeds that can be achieved by alternative logic elements that consume considerably less silicon area. We introduce the concept of a trimming input to a logic function, which is an input to a K-variable function about which Shannon decomposition produces a cofactor having fewer than K -1 variables. We show that trimming inputs occur frequently in circuits and we propose low-cost asymmetric FPGA logic element architectures that leverage the trimming input concept, as well as some other properties of a circuit's AND-inverter graph (AIG) functional representation. We describe synthesis techniques for the proposed architectures that combine a standard cut-based FPGA technology mapping algorithm with two straightforward procedures: 1) Shannon decomposition, and 2) finding non-inverting paths in the circuit's AIG. The proposed architectures exhibit improved logic density versus traditional LUT-based architectures with minimal impact on circuit speed.
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 | 0.000 |
| 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) | 0.002 | 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".