Pipeline-Stage-Resolved Timing Characterization of FPGA and ASIC Implementations of a RISC-V Processor
Bibliographic record
Abstract
This paper presents a pipeline-stage-resolved timing characterization of a 32-bit RISC-V processor implemented on a 20 nm FPGA and a 7 nm FinFET ASIC platform. A unified analysis framework is introduced that decomposes timing paths into logic, routing, and clocking components and maps them to well-defined pipeline-stage transitions. This approach enables systematic comparison of timing behavior across heterogeneous implementation technologies at a microarchitectural level. Using static timing analysis and statistical characterization, the study shows that although both implementations exhibit dominant critical paths in the EX→MEM pipeline transition, their underlying timing mechanisms differ fundamentally. FPGA timing is dominated by routing parasitics and placementdependent variability, resulting in wide slack distributions and sensitivity to routing topology. In contrast, ASIC timing is governed primarily by combinational logic depth and predictable parametric variation across process, voltage, and temperature corners, yielding narrow and stable timing distributions. The results provide quantitative insight into the structural origins of timing divergence between programmable and custom fabrics and demonstrate the effectiveness of pipeline-stageresolved analysis for identifying platform-specific bottlenecks. Based on these findings, the paper derives design implications for achieving predictable timing closure in processor architectures targeting both FPGA and ASIC implementations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".