A Dynamic Memory Allocation Library for High-Level Synthesis
Bibliographic record
Abstract
One impediment to the uptake of high-level synthesis (HLS) design methodologies is their lack of support for constructs frequently employed by software engineers - a primary example being dynamic memory allocation routines. No commercial HLS tool supports these constructs, forcing designers to rewrite programs to remove any dynamic memory allocation function calls (e.g.malloc(), free()), replacing them with statically allocated data. This shortcoming limits the portability of C/C++ descriptions, may introduce software bugs, and forces users to overestimate memory requirements, consuming precious on-chip BRAM resources. We address these problems by extending the capabilities of modern HLS tools through introduction of a tool-independent, HLS-friendly C library of five dynamic memory allocation schemes. Additionally, we developed a benchmark suite to evaluate and compare all five allocation schemes for their performance, area and memory trade-offs. We use the high-level synthesis tool, LegUp, to conduct our experiments. Our results indicate that each allocator in our library is best-suited for certain applications, in terms of performance, area and memory usage. We provide usage guidelines to assist HLS developers in selecting an appropriate allocation scheme.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".