Bibliographic record
Abstract
The rising popularity of high-level synthesis (HLS) is due to the complexity and amount of background knowledge required to design hardware circuits. Despite significant recent advances in HLS research, HLS-generated circuits may be of lower quality than human-expert-designed circuits, from the performance, power, or area perspectives. In this work, we aim to raise circuit performance by introducing a transactional memory (TM) synchronization model to the open-source LegUp HLS tool [1]. LegUp HLS supports the synthesis of multi-threaded software into parallel hardware [4], including support for mutual-exclusion lock-based synchronization. With the introduction of transactional memory-based synchronization, location-specific (i.e. finer grained) memory locks are made possible, where instead of placing an access lock around an entire array, one can place a lock around individual array elements. Significant circuit performance improvements are observed through reduced stalls due to contention, and greater memory-access parallelism. On a set of 5 parallel benchmarks, wall-clock time is improved by 2.0x, on average, by the TM synchronization model vs. mutex-based locks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".