Approaching overhead-free execution on FPGA soft-processors
Bibliographic record
Abstract
Implementing systems on FPGA soft-processors, rather than as custom hardware, eases and accelerates the development process, but at the cost of a great reduction in performance. Orthogonal to limitations in parallelism or clock frequency, this reduction in performance primarily originates in the intrinsic addressing and flow-control overheads of scalar microprocessors, which expend a considerable number of cycles interleaving address calculations and branch decisions within the actual useful work. We present an improved FPGA soft-processor architecture which statically overlaps "overhead" computations and executes them in parallel with the "useful" computations, significantly reducing the number of processor cycles needed to execute sequential programs, while reducing maximum clock frequency to 0.939x of its original value. In addition to eliminating almost all overhead computations, the proposed soft-processor can operate at 500 MHz on the Altera Stratix IV FPGA - 0.909x of the absolute maximum rating. Combined, the high speed and execution efficiency increase the range of FPGA designs amenable to soft-processors rather than custom hardware. We evaluate our cycle count improvements with multiple benchmarks, achieving speedups ranging from 1.07x for control-heavy code, to 1.92x for looping code, never performing worse than the original sequential code, and always performing better than a totally unrolled loop.
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".