On the Power Dissipation of Embedded Memory Blocks Used to Implement Logic in Field-Programmable Gate Arrays
Bibliographic record
Abstract
We investigate the power and energy implications of using embedded FPGA memory blocks 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. In this paper, we measure the effects on power and energy as a function of three architectural parameters: the number of available memory blocks, the size of the memory blocks, and the flexibility of the memory blocks. We show that although embedded memories provide area efficient implementations of many circuits, this technique results in additional power consumption. We also show that blocks containing smaller-memory arrays are more power efficient than those containing large arrays, but for most array sizes, the memory blocks should be as flexible as possible. Finally, we show that by combining physical arrays into larger logical memories, and mapping logic in such a way that some physical arrays can be disabled on each access, can reduce the power consumption penalty. The results were obtained from place and routed circuits using standard experimental physical design tools and a detailed power model. Several results were also verified through current measurements on a 0.13 μ m CMOS FPGA.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".