Rapid design space exploration of multi-clock domain MPSoCs with hybrid prototyping
Bibliographic record
Abstract
This paper presents novel techniques of using hybrid prototyping for early power-performance analysis of MPSoC designs with multiple clock domains. The fundamental idea of hybrid prototyping is to simulate a design with multiple cores by creating an emulation kernel in software on top of a single physical instance of the core. However, so far hybrid prototyping has been limited to homogeneous multicores running at the same clock frequency. Moreover, hybrid prototyping has not yet been demonstrated for efficient design space exploration. Our work focuses on enhancing the capabilities of hybrid prototyping, such that it can be applied to realistic multi-clock MPSoC designs as well to perform early power-performance evaluation of MPSoC designs. Our experiments using industrial strength applications such as JPEG, MP3 and Packet Processing, demonstrate the high accuracy of our hybrid prototypes, and over two orders of magnitude improvement over software simulation speed. We also demonstrate that exploring over 150 design options using hybrid prototyping can be done with high reliability in the order of minutes compared to multiple days using conventional FPGA prototyping.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".