Towards Synthesis-Free JIT Compilation to Commodity FPGAs
Bibliographic record
Abstract
We explore the feasibility of accelerating soft processors by dynamically translating hot segments of code into FPGA circuits. We propose an approach that tackles two key challenges: the prohibitive compile time of standard synthesis tools and the limited run-time reconfigurability of commodity FPGAs. We use traces, or hot straight-line segments of code, as the units of code to translate into FPGA circuits, combined with a pre-synthesized overlay that is tuned for traces. The overlay, referred to as the Virtual Dynamically Reconfigurable (VDR) overlay consists of an array of functional units that are interconnected by a set of programmable switches. The overlay can be rapidly configured by the soft processor at run-time. Our approach avoids traditional synthesis and reduces code-to-circuit translation to the significantly faster mapping of instructions to VDR units. Preliminary evaluation shows that the overlay speeds up the execution of the benchmark by up to 9X over a Nios II processor. The overlay incurs a 6.4X penalty in resources compared to Nios II.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".