Hardware software partitioning using directed acyclic data dependence graph with precedence
Bibliographic record
Abstract
In this thesis we present a system partitioning technique that employs C/C++ as input specification language for hardware/software co-design. The proposed algorithm is able to explore a number of partitioning solutions as compared to other partitioning research. This benefit is obtained by processing data dependency and precedence dependency simultaneously in a new representation called Directed Acyclic Data dependency Graph with Precedence (DADGP). DADGP is an extension of Directed Acyclic Graph (DAG) structure frequently used in the past for partitioning. The DADGP based partitioning algorithm minimizes communication overhead, overall system execution time as well as system cost in terms of hardware area. The algorithm analyzes the DADGP and tries to expose parallelism between processing elements and repeated tasks. The benefits of exposing parallelism with minimum inter PE communication overhead are shown in the experimental results. However, such benefits come with increase in cost due to additional hardware units and their interconnections. DADGP-based partitioning technique is also employed to implement block matching and SOBEL edge detection techniques. Overall, the proposed system partitioning algorithm is fast and powerful enough to handle complicated and large system designs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".