Power Implications of Implementing Logic Using FPGA Embedded Memory Arrays
Bibliographic record
Abstract
This paper investigates the power and energy implications of using embedded FPGA memory arrays to implement logic. Previous studies have shown that this technique provides extremely dense implementations of some types of logic circuits, however, these previous studies did not evaluate the impact on power. The authors measure the effects on power and energy as a function of three architectural parameters: the number of available memory arrays, the size of the memory arrays, and the flexibility of the memory arrays. It was shown in this paper that although embedded memories provide area efficient implementations of many circuits, this technique results in additional power consumption. When power can be traded off for density, it was also shown that for most array sizes, the arrays should be as flexible as possible, and that smaller memory arrays are more power efficient than large arrays. When larger arrays are desired for more density improvement, non-square memories with more rows than columns are better. The results were obtained from fully place and routed circuits using modified versions of VPR and the Poon power model. Several results were also verified through measurements on a 0.13mum CMOS FPGA (Altera Stratix EP1S40)
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".