Energy and Task-Aware Partitioning on Single-ISA Clustered Heterogeneous Processors
Bibliographic record
Abstract
Heterogeneous multi-core processing is increasingly adopted in embedded systems. Heterogeneous platforms can provide energy consumption reduction by employing longstanding techniques like Dynamic Voltage and Frequency Scaling (DVFS) and Dynamic Power Management (DPM). An effective energy-management strategy simultaneously exploits hardware-and software-level energy-reduction techniques. Energy-efficient partitioning is one software-level method where task allocation to heterogeneous clusters directly impacts the total system energy. In this paper, we couple the problem of energy-efficient partitioning on single-ISA heterogeneous platforms with task-aware scheduling. Tasks differ in their instruction mix, cache, memory and I/O access, execution path, and active processing and SoC circuitry. This affects their power demand. We make further use of underlying hardware frequency scaling to reduce the system energy. We propose four variants of our Task and Cluster Heterogeneity Aware Partitioning (TCHAP) targeting ARM big.LITTLE platforms, and show that our algorithms achieve up to 30 percent energy-reduction on average compared to a state-of-the-art scheme.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".