VEGAS
Bibliographic record
Abstract
This paper presents VEGAS, a new soft vector architecture, in which the vector processor reads and writes directly to a scratchpad memory instead of a vector register file. The scratchpad memory is a more efficient storage medium than a vector register file, allowing up to 9x more data elements to fit into on-chip memory. In addition, the use of fracturable ALUs in VEGAS allow efficient processing of bytes, halfwords and words in the same processor instance, providing up to 4x the operations compared to existing fixed-width soft vector ALUs. Benchmarks show the new VEGAS architecture is 10x to 208x faster than Nios II and has 1.7x to 3.1x better area-delay product than previous vector work, achieving much higher throughput per unit area. To put this performance in perspective, VEGAS is faster than a leading-edge Intel processor at integer matrix multiply. To ease programming effort and provide full debug support, VEGAS uses a C macro API that outputs vector instructions as standard NIOS II/f custom instructions.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.097 | 0.039 |
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".