Profiling-driven multi-cycling in FPGA high-level synthesis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Multi-cycling is a well-known strategy to improve performance in digital design, wherein the required time for selected combinational paths is lengthened to multiple clock cycles (rather than just one). The approach can be applied to paths associated with computations whose results are not needed immediately -- such paths are allowed multiple clock cycles to complete, reducing the opportunity for them to form the critical path of the circuit. In this paper, we consider multi-cycling in the high-level synthesis context (HLS) and use software profiling to guide multi-cycling optimizations. Specifically, prior to HLS, we execute the program in software with typical datasets to gather data on the number of times each code segment executes. During HLS, we then extend the schedule for infrequently executed code segments and apply multi-cycling to the dilated schedules, which exhibit greater opportunities for multi-cycling. In essence, our approach ensures that non-frequently executed code segments will not form the critical path of the HLS-generated circuit. In an experimental study targeting the Altera Stratix IV FPGA, we evaluate the impact on speed performance and area for both traditional multi-cycling, as well as the proposed software profiling-driven multi-cycling, and show that profiling-driven multi-cycling leads to an average speedup of over 10% across 13 benchmark circuits, with some circuit speedups in excess of 30%. Circuit area is reduced by 11%, yielding a mean 20% improvement in area-delay product.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 it