Low-Swing Signaling for FPGA Power Reduction (Abstract Only)
Bibliographic record
Abstract
FPGAs are widely used in digital circuits implementation because of their lower non-recurring engineering cost and shorter time-to-market in comparison with ASICs. However, there are still area, performance, and energy efficiency gaps between FPGAs and ASICs. In this work, we propose a new FPGA architecture to narrow the energy efficiency gap. Since more than 62% of FPGA power is consumed in its interconnect, we target power consumption of the interconnect and try to reduce dynamic power consumption of this part using low-swing signaling technique. To implement low-swing signaling, high-to-low and low-to-high voltage level converters are added to the switch boxes and connection blocks of the basic architecture. Simulation results on 20 largest MCNC circuits and 19 computational benchmarks confirm that the proposed architecture achieves an average of 13.5% total power reduction with the cost of less than 1% area and delay overhead. To the authors? knowledge, the proposed architecture in this work is the first architecture that provides low-swing signaling for single driver unidirectional routing scheme. Moreover, the proposed architecture has the maximum CAD tool flexibility and no extra constraint is required for the placement and routing algorithms.
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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 | 0.000 | 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) | 0.015 | 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".