A Regression-Based Approach Towards Estimating the Area, Delay and Leakage Power of Synthesizable FPGA Tiles
Bibliographic record
Abstract
This work addresses the challenges of estimating the area, delay, and power characteristics of standard cell based FPGA tiles. The area, delay and power characteristics of standard cell based FPGA tiles are typically complicated by non-uniform cell composition and sizes due to synthesis optimizations. Traditional methods that rely on summing the area, delay and leakage power consumption of individual components in FPGA tiles face difficulties because they require accurate determination of cell sizes and compositions first. Automated cell sizing approaches have been proposed to tackle this issue, but they often require refinement when new process technologies are introduced. Additionally, running transistor sizing algorithms across a large number of architectures is computationally intensive and time-consuming. In contrast, our approach investigates a holistic regression-based methodology that directly estimates the area, delay and leakage power of an FPGA tile based on its architectural parameters. By utilizing a regression-based model, a small sample of the design space can be synthesized to create a generalized model that can be used to bypass the complex cell sizing and selection process for the remaining designs. This allows for efficient and scalable estimation that adapts more readily to new technologies and architectures without the need for extensive re-synthesis or optimization runs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".