Multi-pumping for resource reduction in FPGA high-level synthesis
Bibliographic record
Abstract
Abstract—Resource sharing is a classic high-level synthesis (HLS) optimization that saves area by mapping multiple oper-ations to a single functional unit. With resource sharing, only operations scheduled in separate cycles can be assigned to shared hardware, which can result in longer schedules. In this paper, we propose a new approach to resource sharing that allows multiple operations to be performed by a single functional unit in one clock cycle. Our approach is based on multi-pumping, which operates functional units at a higher frequency than the surrounding system logic, typically 2×, allowing multiple computations to complete in a single system cycle. Our approach is particularly effective for DSP blocks on an FPGA, which are used to perform multiply and/or accumulate operations. Our results show that resource sharing using multi-pumping is comparable to traditional resource sharing in terms of area saved, but provides significant performance advantages. Specifically, when targeting a 50 % reduction in DSP blocks, traditional resource sharing decreases circuit speed performance by 80%, on average, whereas multi-pumping decreases circuit speed by just 5%. Multi-pumping is a viable approach to achieve the area reductions of resource sharing, with considerably less negative impact to circuit performance. I.
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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 | 0.001 | 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".