FPGA Power Reduction by Guarded Evaluation Considering Logic Architecture
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
Guarded evaluation is a power reduction technique that involves identifying subcircuits (within a larger circuit) whose inputs can be held constant (guarded) at specific times during 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, making the circuitry that generates such signals a candidate for guarding. Guarded evaluation has been demonstrated successfully for application-specific integrated circuits (ASICs); in this paper, we apply the technique to field-programmable gate arrays (FPGAs). In ASICs, guarded evaluation entails adding additional hardware to the design, increasing silicon 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 conditions under which a subcircuit's inputs can be held constant without impacting the larger circuit's functional correctness. We propose a simple solution to this problem based on discovering gating inputs using “noninverting” and “partial noninverting” paths in a circuit's AND-inverter graph representation. Experimental results show that guarded evaluation can reduce switching activity on average by as much as 32% and 25% for 6-input look-up table (6-LUT) and 4-LUT architectures, respectively. Dynamic power consumption in the FPGA interconnect is reduced on average by as much as 24% and 22% for 6-LUT and 4-LUT architectures, respectively. The impact to critical path delay ranges from 1% to 43%, depending on the guarding scenario and the desired power/delay tradeoff.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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