Bibliographic record
Abstract
With increasing core counts and higher memory demands from applications, it is imperative that networks-on-chip (NoCs) provide low-latency, power-efficient communication. Conventional NoCs tend to be over-provisioned for worst-case bandwidth demands leading to ineffective use of network resources and significant power inefficiency; average channel utilization is typically less than 5% in real-world applications. In terms of performance, low-latency techniques often introduce power and area overheads and incur significant complexity in the router microarchitecture. We find that both low latency and power efficiency are possible by relaxing the constraint of lossless communication. This is inspired from internetworking where best effort delivery is commonplace. We propose the Runahead NoC, a lightweight, lossy network that provides single-cycle hops. Allowing for lossy delivery enables an extremely simple bufferless router microarchitecture that performs routing and arbitration within the same cycle as link traversal. The Runahead NoC operates either as a power-saver that is integrated into an existing conventional NoC to improve power efficiency, or as an accelerator that is added on top to provide ultra-low latency communication for select packets. On a range of PAR-SEC and SPLASH-2 workloads, we find that the Runahead NoC reduces power consumption by 1.81 as a power-saver and improves runtime and packet latency by 1.08× and 1.66× as an accelerator.
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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".