Improving the memory footprint and runtime scalability of FPGA CAD algorithms
Bibliographic record
Abstract
Advances in process technology have allowed for a dramatic increase in the capacity of FPGAs and this scaling is continuing at a steady pace. However, this scaling places increasing demands on the FPGA CAD tools. Already, for very large designs, compile-times of an entire work day are common, and memory requirements that exceed what would be found in a common desktop workstation are the norm. As FPGAs continue to grow, the problem will become worse. Unless the scalability of FPGA CAD tools is addressed, the long run times and large memory footprints will become a hindrance to future FPGA scaling, leading to increased costs and design times for companies who use these devices. The proposed research focuses on both the memory and runtime scalability of FPGA CAD tools. We have presented work on effective methods to improve the memory scalability. This work is summarized in Section 3. We are currently focusing on the runtime scalability portion of the project. Our current progress and proposed research on this part was summarized in Section 4.
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 | 0.000 | 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 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".