Modeling Energy-Time Trade-Offs in VLSI Computation
Bibliographic record
Abstract
The performance of today's computers is limited primarily by power consumption rather than the number of instructions executed. Because the energy required to perform an operation using VLSI circuits drops rapidly with the time allowed for the operation, many slow processors can complete a parallel computation using less time and less energy than a fast uniprocessor that can execute the best sequential algorithm. This motivates designing algorithms for minimum execution time subject to energy constraints. We propose a simple model for analyzing algorithms that reflects the energy-time trade-offs of CMOS circuits. Using this model, we derive lower bounds for the energy-constrained execution time of sorting, addition, and multiplication, each with bitwise inputs, and we present algorithms that meet these bounds. These lower bounds are based on the energy-time costs of communication distance, rather than bisectional bandwidth arguments typical of area-time lower bounds. We show that minimizing time under energy constraints is not the same as minimizing operation count or computation depth. This work establishes a tractable method for the evaluation of parallel computations in a power-constrained environment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".