Area, delay, power, and cost trends for metal-programmable structured ASICs (MPSAs)
Bibliographic record
Abstract
As integrated circuits are scaled to finer process geometries, the risk involved with the increased design effort and high NRE costs becomes too great for some applications. FPGAs offer one solution, but for high performance, high volume, or low power applications, FPGAs may not be suitable. For some of these applications, structured ASICs may provide a better solution. Structured ASICs share many of the same characteristics as FPGAs, but consume less power, are more dense, and can run faster. Despite these advantages, structured ASICs have not yet achieved the level of popularity some had predicted. There are several possible reasons, including unfamiliar technology, immature CAD, and claimed advantages which have not yet been concretely demonstrated. In much the same way that it has helped improve FPGA adoption, we believe that an increased public research effort can begin to address many of these issues. This paper takes a step in this direction by investigating metal-programmable structured ASICs, or MPSAs. We determine the area, delay, and power trends and quantify the cost advantages of MPSAs relative to cell based ICs (CBICs) for a wide range of possible MPSA logic architectures and layout assumptions. In particular, we quantify the impact of the number of user-defined metal mask layers on these metrics. Results suggest the number of these programmable layers should be as small as possible for most MPSAs, unless very large die sizes are required.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".