NCOR: An FPGA-Friendly Nonblocking Data Cache for Soft Processors with Runahead Execution
Bibliographic record
Abstract
Soft processors often use data caches to reduce the gap between processor and main memory speeds. To achieve high efficiency, simple, blocking caches are used. Such caches are not appropriate for processor designs such as Runahead and out-of-order execution that require nonblocking caches to tolerate main memory latencies. Instead, these processors use non-blocking caches to extract memory level parallelism and improve performance. However, conventional non-blocking cache designs are expensive and slow on FPGAs as they use content-addressable memories (CAMs). This work proposes NCOR, an FPGA-friendly non-blocking cache that exploits the key properties of Runahead execution. NCOR does not require CAMs and utilizes smart cache controllers. A 4 KB NCOR operates at 329 MHz on Stratix III FPGAs while it uses only 270 logic elements. A 32 KB NCOR operates at 278 Mhz and uses 269 logic elements.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".