Accurate Power Estimation Identity for DSP Blocks Targeted to FPGAs
Bibliographic record
Abstract
Nowadays, the main challenge in front of system designers is to design power-efficient systems with reduced design turnaround time. It can be achieved in two ways, firstly, utilize off-shelf components (Intellectual Property cores) along with user-defined IPs. Secondly, estimate the power at an early stage of the design cycle. Therefore, this paper represents the power estimation of Cascaded and Non-Cascaded DSP blocks based on IP modeling. The DSP blocks are designed using a blend of embedded and user-defined IP cores. Curve-fitting and regression-based models for power evaluation have been created for each IP core. The power of the complete DSP block is estimated using identity projected by Elleouet et al. by incorporating the power values of each IP core obtained from the regression-based models. The models have been validated for accuracy using the power values gained from the commercial tool (Vivado design suite (2014.2)). From the analysis, it has been found that the identity is providing inaccurate results for cascaded DSP blocks. Therefore, in this work, a new identity has been proposed that has been estimating the power of the cascaded systems accurately and also in alignment with the results of a commercial tool.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".