On the sensitivity of FPGA architectural conclusions to experimental assumptions, tools, and techniques
Bibliographic record
Abstract
Recent years have seen a tremendous increase in the capacities and capabilities of Field-Programmable Gate Arrays (FPGA's). Much of this dramatic improvement has been the result of changes to the FPGAs' internal architectures. New architectural proposals are routinely generated in both academia and industry. For FPGA's to continue to grow, it is important that these new architectural ideas are fairly and accurately evaluated, so that those worthy ideas can be included in future chips. Typically, this evaluation is done using experimentation. However, the use of experimentation is dangerous, since it requires making assumptions regarding the tools and architecture of the device in question. If these assumptions are not accurate, the conclusions from the experiments may not be meaningful. In this paper, we investigate the sensitivity of FPGA architectural conclusions to experimental variations. To make our study concrete, we evaluate the sensitivity of four previously published and well-known FPGA architectural results: lookup-table size, switch block topology, cluster size, and memory size. It is shown that these experiments are significantly affected by the assumptions, tools, and techniques used in the experiments.
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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 | 0.031 | 0.265 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".