Improved language support for Verilog elaboration in Odin II and FPGA architecture benchmarking in the VTR CAD tool
Bibliographic record
Abstract
Field-programmable gate arrays (FPGAs) are integrated circuits that can be designed or configured after manufacturing. They are used in many disciplines to create prototypes of hardware or in applications where hardware functionality needs to be changed more frequently. Design of new FPGA architectures requires tools that allow developers to create new structures and test those structures in order to compare the results to already established solutions. Boolean circuits, implemented on the FPGAs are compiled using hardware description languages such as Verilog or VHDL. The VTR (Verilog to Routing) CAD (Computer Aided Design) tool, compiles Verilog source code that targets specific hardware resources as FPGAs and ASICs (Application Specific Integrated Circuits). The VTR CAD tool consists of three tools: Odin II, for elaboration from Verilog to a netlist, ABC, for logic synthesis, and VPR, for physical synthesis and analysis. Odin II currently supports only a sub-set of constructs in Verilog language. This paper describes improved and expanded language support for Verilog elaboration introduced in Odin II, in order to provide developers with a tool set to assist in modern FPGA research. With this enhanced language support, a subsequent evaluation of the performance characteristics of VTR flow with a set of benchmarks that are supported by VTR is performed.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".