Bibliographic record
Abstract
Guarded evaluation is a power reduction technique that in-volves identifying sub-circuits (within a larger circuit) whose inputs can be held constant (guarded) at specific times dur-ing circuit operation, thereby reducing switching activity and lowering dynamic power. The concept is rooted in the property that under certain conditions, some signals within digital designs are not “observable ” at design outputs, mak-ing the circuitry that generates such signals a candidate for guarding. Guarded evaluation has been demonstrated successfully for custom ASICs; in this paper, we apply the technique to FPGAs. In ASICs, guarded evaluation entails adding additional hardware to the design, increasing sili-con area and cost. Here, we apply the technique in a way that imposes minimal area overhead by leveraging existing unused circuitry within the FPGA. The primary challenge in guarded evaluation is in determining the specific condi-tions under which a sub-circuit’s inputs can be held con-stant without impacting the larger circuit’s functional cor-rectness. We propose a simple solution to this problem based on discovering “non-inverting paths ” in the circuit’s AND-inverter graph representation. Experimental results show that guarded evaluation can reduce switching activity by 22%, on average, and can reduce power consumption in the FPGA interconnect by 14%.
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) | 0.003 | 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 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".