ASAP: Automatic Sizing and Partitioning for Dynamic Memory Heaps in High-Level Synthesis
Bibliographic record
Abstract
Efficient high-level synthesis (HLS) of dynamic memory allocation techniques (malloc() and free()) simplifies the compilation of algorithms with runtime-varying memory requirements to hardware designs. Existing HLS memory allocation frameworks often degrade performance and area, while simultaneously introducing even more parameters to optimize (e.g. heap depth, heap assignments to program logic). We address these concerns with ASAP (Automatic Sizing and Partitioning), a dynamic memory allocation framework for HLS tools. ASAP provides (1) automatic heap depth selection through dynamic analysis of an application, (2) automatic heap partitioning (through static analysis) to provide parallelism from program logic to memory, improving performance. We demonstrate that ASAP is able to improve performance and reduce cycle latencies compared with non-heap-partitioned designs, with speed-ups up to ~ 5× when applied to common memory patterns, and up to ~ 2× improvement when applied to a suite of dynamic-memory intensive applications.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".