Extending Force-Directed Scheduling with Explicit Parallel and Timed Constructs for High-Level Synthesis
Bibliographic record
Abstract
This work extends force-directed scheduling (FDS) to support specification constructs that express parallelism and timing behaviours. We select the FDS algorithm because it maximizes the amount of resource sharing, and it naturally supports constructs for parallelism. However, timed constructs are not supported. As a result, we propose timed FDS (TFDS) that optimizes over parallel, timed and untimed constructs. In doing so, we make the following four contributions: 1) we extend the definition of control data flow graphs (CDFGs) to define timed CDFGs (TCDFGs), 2) we define a scheduling algorithm for timed constructs called TIME, 3) we extend the definition of mobility used in FDS, and 4) we present optimizations for a composition of parallel, timed and untimed constructs to better aid FDS. We implement our extensions in a high-level synthesis framework based on the abstract state machine formalism, and we generate synthesizable VHDL. We experiment with several examples such as FIR, edge detector, and a differential equation solver, and target them onto an Altera DE2 FPGA. Some of these experiments show improvements of up to 52% in circuit area when compared to their unoptimized counterparts.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".