Exploring Writeback Designs for Efficiently Leveraging Parallel-Execution Units in FPGA-Based Soft-Processors
Bibliographic record
Abstract
Maximizing processor performance depends on maximizing the product of instruction throughput and clock frequency. Writeback mechanisms and forwarding networks heavily impact both of these properties along with the resource usage and scalability of the processor design. Furthermore, these mechanisms are typically multiplexer heavy which can make their implementation resource inefficient on FPGAs. In this paper, we explore multiple different writeback and result storage mechanisms using an FPGA-based RISC-V soft-processor (Taiga), exploring both exception-safe and non-exception-safe designs. Writeback mechanisms based on per-unit result storage and centralized storage are explored while leveraging FPGA specific resources such as LUTRAMs. We evaluate the designs based on their impact on instruction throughput, processor frequency, and scalability of both simultaneous instructions in-flight and the number of execution units. As each design has different characteristics, we focus on comparing and contrasting the designs. We find that across all designs, average IPC can vary by up to 11%, with a few designs reaching the maximum IPC of one for some benchmarks. Clock frequency is found to vary by up to 20% across the designs, but is not significantly impacted when increasing the number of execution units. Scaling up the instructions in-flight is found to have the greatest variability, with LUT usage increasing by 3% to 93% across the different designs. Overall, we find that under current constraints, a commit-buffer design provides the highest combination of performance and performance per LUT.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".