Bibliographic record
Abstract
Multicore platforms are becoming increasingly pervasive in modern embedded systems. System level modeling techniques have enabled creation of fast software models of multicore platforms, commonly known as Virtual Prototypes, for early functional validation of embedded software, before the hardware is available. On the other hand, for accurate performance validation, the complete multicore platform can be implemented as a physical prototype on FPGA. Both virtual platforms and FPGA prototypes have their respective pros and cons. Virtual platforms have the advantage of high speed functional simulation and, typically, scale well with the number of cores. However, the accuracy of performance estimation is sacrificed. FPGA prototypes provide cycle-accurate performance estimation, because the software executes directly on an FPGA implementation of the target cores. However, it takes a significant amount of time to design, implement and test the inter-core communication architecture on the FPGA. \nIn this thesis we propose to design a novel system-level modeling framework, called Hybrid Prototyping. Our goal is to provide the benefits of both virtual platforms and FPGA prototypes. It aims to provide early, fast, and scalable models, similar to virtual platforms, along with the cycle-accuracy of FPGA prototypes. Using hybrid prototyping, embedded software designers will be able to create concurrent applications and accurately analyze the performance implication of their optimizations before the chip is delivered. At the same time, multicore architects will be able to modify the platform model without having to do full system prototyping. Therefore, hybrid prototyping will enable early and reliable multicore embedded system design, resulting in huge productivity gains for both embedded software designers and multicore chip architects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".