Hardware/software partitioning and pipelined scheduling on runtime reconfigurable FPGAs
Bibliographic record
Abstract
FPGAs are widely used in today's embedded systems design due to their low cost, high performance, and reconfigurability. Partially RunTime-Reconfigurable (PRTR) FPGAs, such as Virtex-2 Pro and Virtex-4 from Xilinx, allow part of the FPGA area to be reconfigured while the remainder continues to operate without interruption, so that HW tasks can be placed and removed dynamically at runtime. We address two problems related to HW task scheduling on PRTR FPGAs: (1) HW/SW partitioning. Given an application in the form of a task graph with known execution times on the HW (FPGA) and SW (CPU), and known area sizes on the FPGA, find an valid allocation of tasks to either HW or SW and a static schedule with the optimization objective of minimizing the total schedule length (makespan). (2) Pipelined scheduling. Given an input task graph, construct a pipelined schedule on a PRTR FPGA with the goal of maximizing system throughput while meeting a given end-to-end deadline. Both problems are NP-hard. Satisfiability Modulo Theories (SMT) is an extension to SAT by adding the ability to handle arithmetic and other decidable theories. We use the SMT solver Yices with Linear Integer Arithmetic (LIA) theory as the optimization engine for solving the two scheduling problems. In addition, we present an efficient heuristic algorithm based on kernel recognition for the pipelined scheduling problem, a technique borrowed from SW pipelining, to overcome the scalability problem of the SMT-based optimal solution technique.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".