Application du concept des transactions pour la modelisation et la simulation multicoeur des systemes sur puce
Bibliographic record
Abstract
With the increasing complexity of SoCs, new challenges continue to emerge in the design of these systems in terms of formal verification and high-level synthesis. Several research efforts around SystemC, considered the de facto standard for system-level design, are underway to meet these new challenges. However, because of the complex concurrency model of SystemC, these challenges remain difficult tasks. Thus, we believe it is important to continue on a better footing by using a more effective concurrency model. Therefore, in this thesis, we study a design methodology that provides a better abstraction for modeling parallel components based on the concept of transaction. We show how, through simple reasoning about transactions, it becomes easier to apply formal verification, incremental refinement and high-level synthesis. In order to evaluate the effectiveness of this methodology, we set the goal to optimize the simulation speed of a transactional model by taking advantage of a multicore machine. We present a modeling and parallel simulation environment that we developed. We study different scheduling strategies in terms of parallelism and synchronization overhead. An experiment made on a Wi-Fi 802.11a transmitter model achieved a speed up of about 1.8 using two threads. With 8 threads, although the workload of individual transactions was not significant, we could reach a speed up equal to 4.6 which is a very promising result. Keywords: SOC Design, Parallel Simulation, Transactions, Multi-core
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".