Exploring Area and Delay Tradeoffs in FPGAs With Architecture and Automated Transistor Design
Bibliographic record
Abstract
Field-programmable gate arrays (FPGAs) are used in a variety of markets that have differing cost, performance and power consumption requirements. While it would be ideal to serve all these markets with a single FPGA family, the diversity in the needs of these markets means that generally more than one family is appropriate. Consequently, FPGA vendors have moved to provide a diverse set of families that sit at different points in the area-speed-power design space. This paper aims to understand the circuit and architectural design attributes of FPGAs that enable tradeoffs between area and speed, and to determine the magnitude of the possible tradeoffs. This will be useful for architects seeking to determine the number of device families in a suite of offerings, as well as the changes to make between families. We explore a broad range of architectures and circuit designs and developed a transistor sizing tool that automatically optimizes each design. In this paper, we describe this tool and demonstrate that it achieves results that are comparable to past work but with vastly less effort. We then use the designs produced by the tool to explore the range of tradeoffs possible. We find that through architecture and transistor sizing changes it is possible to usefully vary the area of an FPGA by a factor of 2.0 and the performance of an FPGA by a factor of 2.1. We also observe that the range of area and delay tradeoffs possible by varying only the transistor sizing of a single architecture is larger than the ranges observed in past architectural experiments. In addition to transistor size, we note that LUT size is one of the most useful parameters for trading off area and delay.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".