Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
While soft processors are increasingly common in FPGA-based embedded systems, it remains a challenge to scale their performance. We propose extending soft processor instruction sets to include support for vector processing. The resulting system of vectorized software and soft vector processor hardware is (i) portable to any FPGA architecture and vector processor configuration, (ii) scalable to larger yet higher-performance designs, and (iii) flexible, allowing the underlying vector processor to be customized to match the needs of each application. Using our robust and verified parameterized vector processor design and industry-standard EEMBC benchmarks, we evaluate the performance and area trade-offs for different soft vector processor configurations using an FPGA development platform with DDR SDRAM. We find that on average we can scale performance from 1.8x up to 6.3x for a vector processor design that saturates the capacity of our platform's Stratix 1S80 FPGA. We also automatically generate application-specific vector processors with reduced datapath width and instruction set support which combined reduce the area by up to 70% (61% on average) without affecting performance.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it