Efficient methods for out-of-order load/store execution for high-performance soft processors
Bibliographic record
Abstract
As FPGAs continue to increase in size, it becomes increasingly feasible and desirable to build higher performance soft processors. Preserving the familiar single-threaded programming model can be done with an out of order processor. The ability to execute memory loads and stores out of order has a large impact on performance, but this is difficult to do because the dependencies between stores and loads are not known until addresses are computed. Out of order memory disambiguation is traditionally done with CAMs in the load queue and store queue, but large CAMs are inefficient on FPGAs. Store Queue Index Prediction (SQIP) and NoSQ propose to replace CAMs with store-load forwarding prediction and load re-execution. We implement four memory disambiguation schemes (in-order, CAM, SQIP, NoSQ) on a Stratix IV FPGA and evaluate the area and delay trade-offs. We find that CAM area and delay degrade quickly with load/store queue size, while SQIP and NoSQ have little degradation with queue size but have area overhead for prediction and predictor training hardware. SQIP and NoSQ use less area than CAMs beyond 32 and 16 load/store queue entries, respectively, and have higher maximum frequency beyond 4 entries.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.006 | 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".