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
Benchmarks play a key role in Field-Programmable Gate Array (FPGA) architecture and CAD research, enabling the quantitative comparison of tools and architectures. It is important that these benchmarks reflect modern large-scale systems that make use of heterogeneous resources; however, most current FPGA benchmarks are both small and simple. In this artile, we present Titan, a hybrid CAD flow that addresses these issues. The flow uses Altera’s Quartus II FPGA CAD software to perform HDL synthesis and a conversion tool to translate the result into the academic Berkeley Logic Interchange Format (BLIF). Using this flow, we created the Titan23 benchmark set, which consists of 23 large (90K--1.8M block) benchmark circuits covering a wide range of application domains. Using the Titan23 benchmarks and an enhanced model of Altera’s Stratix IV architecture, including a detailed timing model, we compare the performance and quality of VPR and Quartus II targeting the same architecture. We found that VPR is at least 2.8 × slower, uses 6.2 × more memory, 2.2 × more wire, and produces critical paths 1.5 × slower compared to Quartus II. Finally, we identified that VPR’s focus on achieving a dense packing and an inability to take apart clusters is responsible for a large portion of the wire length and critical path delay gap.
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