PoCo: Extending Task-Parallel HLS Programming with Shared Multi- <i>P</i> r <i>o</i> ducer Multi- <i>Co</i> nsumer Buffer Support
Bibliographic record
Abstract
Advancements in High-Level Synthesis (HLS) tools have enabled task-level parallelism on FPGAs. However, prevailing frameworks predominantly employ Single-Producer-Single-Consumer (SPSC) models for task communication, thus limiting application scenarios. Analysis of designs becomes non-trivial with an increasing number of tasks in task-parallel systems. Adding features to existing designs often requires re-profiling of several task interfaces, redesign of the overall inter-task connectivity, and describing a new floorplan. This article proposes PoCo, a novel framework to design scalable Multi-Producer-Multi-Consumer (MPMC) models on task-parallel systems. PoCo introduces a shared-buffer abstraction that facilitates dynamic and high-bandwidth access to share on-chip memory resources, incorporates latency-insensitive communication, and implements placement-aware design strategies to mitigate routing congestion. The frontend provides convenient APIs to access the buffer memory, while the backend features an optimized and pipelined datapath. Empirical evaluations demonstrate that PoCo achieves up to 50% reduction in on-chip memory utilization on SPSC models without performance degradation. Additionally, three case studies on distinct real-world applications reveal up to 1.5 \(\times\) frequency improvements and simplified dataflow management in heterogeneous FPGA accelerator designs.
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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 | 0.001 |
| 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 | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".