Using reconfigurability to achieve real-time profiling for hardware/software codesign
Bibliographic record
Abstract
Embedded systems combine a processor with dedicated logic to meet design specifications at a reasonable cost. The attempt to amalgamate two distinct design environments introduces many problems, one being how to partition a single design for the two platforms to achieve the best performance with the least effort. Since the latest FPGA technology allows the integration of soft or hard CPU cores with dedicated logic on a single chip, this presents new opportunities for addressing hardware/software codesign issues in the FPGA design process by utilizing the reconfigurable environment.This paper introduces SnoopP, a non-intrusive, real time, profiling tool. The user is able to obtain a clock cycle accurate profile of the real time performance of a software program running on a soft-core processor instantiated on an FPGA. SnoopP is an essential tool for hardware/software codesign on a reconfigurable platform. It allows the user to quickly obtain accurate profiling information that may greatly influence the partitioning of the design.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".