Performance and Cost Tradeoffs in Metal-Programmable Structured ASICs (MPSAs)
Bibliographic record
Abstract
As process technology scales, the design effort and nonrecurring engineering (NRE) costs associated with the development of integrated circuits is becoming extremely high. Structured ASICs offer one solution to these problems. However, to realize their full potential, their performance and cost advantages, architectures, and CAD must be fully understood. We believe that this can lead to wider adoption of structured ASICs. In this paper, we take a step in this direction and investigate the area, delay, power, and cost tradeoffs in metal-programmable structured ASICs (MPSAs). In particular, we quantify the impact of the number of user-defined (custom) metal mask layers on these metrics. Results indicate that for lowest cost, the number of custom layers should be minimized, especially for small die sizes (e.g., less than 100${\hbox {mm}}^{2}$). Delay and power, however, can be improved by a few additional custom layers. With two custom metal layers, MPSAs can be 2$\times$–10$\times$cheaper than cell-based ICs (CBICs).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".