Quantifying the Gap Between FPGA and Custom CMOS to Aid Microarchitectural Design
Bibliographic record
Abstract
This paper compares the delay and area of a comprehensive set of processor building block circuits when implemented on custom CMOS and FPGA substrates, then uses these results to show how soft processor microarchitectures should be different from those of hard processors. We find that the ratios of the custom CMOS versus FPGA area for different building blocks varies considerably more than the speed ratios, thus, area ratios have more impact on microarchitecture choices. Complete processor cores on an FPGA use 17-27 × more area (“area ratio”) than the same design implemented in custom CMOS. Building blocks with dedicated hardware support on FPGAs such as SRAMs, adders, and multipliers are particularly area-efficient (2-7×), while multiplexers and content-addressable memories (CAM) are particularly area-inefficient (>100×). Applying these results, we find out-of-order soft processors should use physical register file organizations to minimize CAM size.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".