Architecture description and packing for logic blocks with hierarchy, modes and complex interconnect
Bibliographic record
Abstract
The development of future FPGA fabrics with more sophisticated and complex logic blocks requires a new CAD flow that permits the expression of that complexity and the ability to synthesize to it. In this paper, we present a new logic block description language that can depict complex intra-block interconnect, hierarchy and modes of operation. These features are necessary to support modern and future FPGA complex soft logic blocks, memory and hard blocks. The key part of the CAD flow associated with this complexity is the packer, which takes the logical atomic pieces of the complex blocks and groups them into whole physical entities. We present an area-driven generic packing tool that can pack the logical atoms into any heterogeneous FPGA described in the new language, including many different kinds of soft and hard logic blocks. We gauge its area quality by comparing the results achieved with a lower bound on the number of blocks required, and then illustrate its explorative capability in two ways: on fracturable LUT soft logic architectures, and on hard block memory architectures. The new infrastructure attaches to a flow that begins with a Verilog front-end, permitting the use of benchmarks that are significantly larger than the usual ones, and can target heterogenous FPGAs.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".