Software implementation strategies for power-conscious systems
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
A variety of systems with possibly embedded computing power, such as small portable robots, hand-held computers, and automated vehicles, have power supply constraints. Their batteries generally last only for a few hours before being replaced or recharged. It is important that all design efforts are made to conserve power in those systems. Energy consumption in a system can be reduced using a number of techniques, such as low-power electronics, architecture-level power reduction, compiler techniques, to name just a few. However, energy conservation at the application software-level has not yet been explored. In this paper, we show the impact of various software implementation techniques on energy saving. Based on the observation that different instructions of a processor cost different amount of energy, we propose three energy saving strategies, namely (i) assigning live variables to registers, (ii) avoiding repetitive address computations, and (iii) minimizing memory accesses. We also study how a variety of algorithm design and implementation techniques affect energy consumption. In particular, we focus on the following aspects: (i) recursive versus iterative (with stacks and without stacks), (ii) different representations of the same algorithm, (iii) different algorithms - with identical asymptotic complexity - for the same problem, and (iv) different input representations. We demonstrate the energy saving capabilities of these approaches by studying a variety of applications related to power-conscious systems, such as sorting, pattern matching, matrix operations, depth-first search, and dynamic programming. From our experimental results, we conclude that by suitably choosing an algorithm for a problem and applying the energy saving techniques, energy savings in excess of 60% can be achieved.
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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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