Decomposition-Based Vectorless Toggle Rate Computation for FPGA Circuits
Bibliographic record
Abstract
This paper presents a novel and accurate method of estimating the toggle rates of signals in field-programmable gate array (FPGA)-based logic circuits without the use of simulation vectors. Compared to previous vectorless techniques, our approach provides improved accuracy-of-results, especially for individual signals, which could be leveraged by computer-aided design (CAD) tools for performing power optimization of logic circuits. Increased accuracy is achieved by using stochastic methods that estimate the transition densities at FPGA logic elements while accounting for both spatial and temporal correlation of logic signals. Spatial correlation is calculated by leveraging a unique XOR-based decomposition technique that provides both accurate results and fast computation times. We also consider the delay information of implemented circuits, providing for a comprehensive treatment of glitches, including the effects of inertial limits on power dissipation. Our toggle-rate estimation approach has been tested on a commonly used set of Microelectronic Center of North Carolina circuits, as well as a set of industrial circuits targeted to Altera Stratix II FPGAs. Results show that our techniques provide a three times lower percent error, while maintaining a low processing time, when compared to two existing techniques: the vectorless estimation tool shipped with the commercial Quartus II 8.0 CAD tool, and the ACE v2.0 academic tool produced from the University of British Columbia, Vancouver, BC, Canada.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".