HW/SW co-design architecture exploration for VLSI maze routing
Bibliographic record
Abstract
The advance in FPGA technology allowed embedding different types of resources on a single chip. These resources range from a simple look-up table to a complete processor. The resources available on the FPGA fabric allow building various hardware systems for different applications with several trade-offs in terms of performance and power consumption. This paper proposes six different architectures to implement VLSI maze routing algorithm on FPGAs. These architectures utilize two processors (MicroBlaze and Power-PC) and a separate hardware accelerator. The hardware accelerator was designed for the maze routing algorithm with two different protocols for data transfer. All architectures are evaluated based on seven benchmarks. The evaluation includes the performance and the power consumption of the architectures. This work demonstrate that the architecture composed of a soft-core processor directly connected to the hardware accelerator with fully utilized FIFO channel achieves the best power-delay product among all the investigated architectures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".