Design, layout and verification of an FPGA using automated tools
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Creating a new FPGA is a challenging undertaking because of the significant effort that must be spent on circuit design, layout and verification. It currently takes approximately 50 to 200 person years from architecture definition to tape-out for a new FPGA family. Such a lengthy development time is necessary because the process is primarily done manually. Simplifying and shortening the design process would be advantageous since it could reduce the time to market for new FPGAs while also enhancing architecture explorations. One way to accomplish this is through automation and, in this paper, we describe our efforts to automate the entire process by making use of a previously developed set of tools that assist in the creation of the repeatable FPGA tile [25]. Our aim is to demonstrate the feasibility of a CAD flow that uses an input FPGA architecture description to generate a layout that can be sent for fabrication. We prove the feasibility of this proposition by actually designing and fabricating a complete FPGA. Initial functional testing of the FPGA appears promising but is inconclusive at this time. Through this architecture to layout process, we investigate the issues that are faced in the architecture selection, circuit design, layout and verification of such an automatically produced FPGA. We found that there are significant savings in design time. As well, we demonstrate that we can produce a layout using automated tools that is only 36% larger than a commercial FPGA device layout. Given the significant time savings and the relatively minor area penalty, we feel that this work demonstrates that automated layout of FPGAs is practical and advantageous.
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.
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 | 0.000 | 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 it