Energy Optimization for Many-Core Platforms: Communication and PVT Aware Voltage-Island Formation and Voltage Selection Algorithm
Bibliographic record
Abstract
In this paper, we propose a novel approach to voltage-island formation, for the energy optimization of many-core architectures, which mitigates the impact of process, voltage, and temperature (PVT) variations. The islands are created by balancing their shape constraints imposed by intra and inter-island communication with the desire to limit the spatial extent of each island to minimize PVT impact. In addition, to reduce the number of voltage levels in the design, we propose an efficient voltage selection approach that provides near optimal results, for a set of 33 examined cases, with more than a ten times speedup compared to the best-known previous methods. This run-time improvement is important, especially for large many-core platforms. Finally, we present an evaluation platform considering pre-fabrication and post-fabrication PVT scenarios where multiple applications with hundreds to thousands of tasks are mapped onto many-core platforms with hundreds to thousands of cores to evaluate the proposed techniques. Results show that the average energy savings for 33 test cases using the proposed methods are 37% compared to 16% obtained using previous methods.
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.
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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".