Fine-Grained Bandwidth Adaptivity in Networks-on-Chip Using Bidirectional Channels
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Networks-on-Chip (NoC) serve as efficient and scalable communication substrates for many-core architectures. Currently, the bandwidth provided in NoCs is over provisioned for their typical usage case. In real-world multi-core applications, less than 5% of channels are utilized on average. Large bandwidth resources serve to keep network latency low during periods of peak communication demands. Increasing the average channel utilization through narrower channels could improve the efficiency of NoCs in terms of area and power, however, in current NoC architectures this degrades overall system performance. Based on thorough analysis of the dynamic behaviour of real workloads, we design a novel NoC architecture that adapts to changing application demands. Our architecture uses fine-grained bandwidth-adaptive bidirectional channels to improve channel utilization without negatively affecting network latency. Running PARSEC benchmarks on a cycle-accurate full-system simulator, we show that fine-grained bandwidth adaptivity can save up to 75% of channel resources while achieving 92% of overall system performance compared to the baseline network, no performance is sacrificed in our network design configured with 50% of the channel resources used in the baseline.
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.
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 | 0.001 | 0.000 |
| 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) | 0.000 | 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 it