Energy-Efficient Hybrid Unicore Architecture In Future Embedded Chip-Multiprocessor
Bibliographic record
Abstract
Recently, technology scaling has enabled the placement of an increasing number of cores, in the form of chip-multiprocessors (CMPs) on a chip and continually shrinking transistor sizes to improve performance. In this context, power consumption has become the main constraint in designing CMPs. As a result, uncore components power consumption taking increasing portion from the on-chip power budget; therefore, designing power management techniques, particularly memory and network-on-chip (NoC) systems, has become an important issue to solve. Consequently, a considerable attention has been directed toward power management based on CMPs components, particularly shared caches and uncore interconnected structures, to overcome the challenges of limited chip power budget.<div>This work targets to design an energy-efficient uncore architecture by using heterogeneity in components (cache cells) and operational parameters (Voltage/Frequency). In order to ensure the minimum impact on the system performance, a run-time approach is investigated to assess the proposed method. An architecture is proposed where the cache layer contains the heterogenous cache banks in all placed in one frequency voltage domain. Average memory access time (AMAT) was selected as a network monitor to monitor the performance on the run-time. The appropriate size and type of the last level cache (LLC) and Voltage/Frequency for the uncore domain is adjusted according to the calculated AMAT which indicates the system demand from the uncore.<br></div><div>The proposed hybrid architecture was implemented, investigated and compared with the a baseline model where only SRAM banks were used in the last level cache. Experimental results on the Princeton Application Repository for Shared-Memory Computers (PARSEC) benchmark suit,show that the proposed architecture yields up to a 40% reduction in overall chip energy-delay product with a marginal performance degradation in average of -1.2% below the baseline one. The best energy saving was 55% and the worse degradation was only 15%.<br></div>
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".