Microarchitecture and Circuits for a 200 MHz Out-of-Order Soft Processor Memory System
Bibliographic record
Abstract
Although FPGAs have grown in capacity, FPGA-based soft processors have grown very little because of the difficulty of achieving higher performance in exchange for area. Superscalar out-of-order processors promise large performance gains, and the memory subsystem is a key part of such a processor that must help supply increased performance. In this article, we describe and explore microarchitectural and circuit-level tradeoffs in the design of such a memory system. We show the significant instructions-per-cycle wins for providing various levels of out-of-order memory access and memory dependence speculation (1.32 × SPECint2000) and for the addition of a second-level cache (another 1.60 × ). With careful microarchitecture and circuit design, we also achieve a L1 translation lookaside buffers and cache lookup with 29% less logic delay than the simpler Nios II/f memory system.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".