Exploiting multi-band transmission line interconnects to improve the efficiency of cache coherence in multiprocessor system-on-chip
Bibliographic record
Abstract
Main-stream general-purpose microprocessors integrate a growing number of cores on-chip, requiring high-performance interconnects and efficient cache coherence for data transmission and sharing. Conventional directory-based cache coherence has high indirection overhead, which adds to the critical path of data requests and lowers overall system performance. In fact, with globally shared high-performance interconnects, cache coherence could be optimized and the indirection overhead could be relieved. This paper explores the use of multi-band transmission lines to implement globally shared interconnects. Taking advantage of the aggregate frequency band resources, the proposed interconnect supports efficient multi-cast, and helps improve the efficiency of cache coherence with augmented parallelism. Coherence indirections are avoided, and we have seen an average of 17% boost in application performance, as well as an average of 18% throughput improvement compared to an implementation of conventional cache coherence on single-band transmission line based interconnects.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".