Fast FPGA-based area and latency estimation for a novel hardware/software partitioning scheme
Why this work is in the frame
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Bibliographic record
Abstract
In this paper a fast and accurate area and latency estimation tool for FPGA-based designs is presented. The tool is developed in the context of a HW/SW partitioning tool. Rather than modeling the hardware implementation as a single alternative, our approach for HW/SW partitioning models the hardware as two extreme alternatives that bound latency range for different hardware implementations. The presented estimation tool estimates the area and latency for these two hardware alternatives. The computational cost of the presented estimation tool depends linearly on the design complexity, and hence, it is very useful for fast design space exploration. Testing this estimation tool on several designs showed that this tool is also accurate. Area estimations are within plusmn7.5% of the actual number of logic elements consumed with an average error of 3.2% for Cyclone FPGAs and 3.5% for Stratix FPGAs.
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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 | 0.001 |
| 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 it