Energy and Task-Aware Partitioning on Single-ISA Clustered Heterogeneous Processors
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
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.
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 | 0.000 | 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) | 0.000 | 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