An Energy-Efficient Reliable Heterogeneous Uncore Architecture for Future 3D Chip-Multiprocessors
Bibliographic record
Abstract
Uncore components such as cache hierarchy and on-chip interconnects consume a significant portion of overall energy consumption in emerging embedded processors. In Nanoscale technologies, static power consumption due to leakage current has become a serious issue in the design of SRAM-based on-chip cache memories and interconnections. To address this issue, non-volatile memory technologies such as STT-RAMs have been proposed as a replacement for SRAM cells due to their near-zero static power and high memory density. Nonetheless, STT-RAMs suffer from some failures such as read-disturb and limited endurance as well as high switching energy. One effective way to decrease the STTRAMs’ switching energy is to reduce their retention time; however, reducing the retention time has a negative impact on the reliability of STT-RAM cells. In this paper, we propose a heterogeneous last level cache (LLC) architecture for 3D embedded chip-multiprocessors (3D eCMPs) which employs two types of STT-RAM memory banks with retention time of 1s and 10ms to provide a beneficial trade-off between reliability, energy consumption, and performance. To this end, we also propose a convex optimization model to find the optimal configurations for these two kinds of memory banks. In parallel with hybrid memory architecting, optimizing the number and placement of through silicon vias (TSVs) as a main component of on-chip interconnection for building 3D CMPs is another important target of the proposed optimization approach. Experimental results show that the proposed method improves the energy-delay products and throughput by about 69% and 34.5% on average compared with SRAM configurations.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".