Optical Ring Network-on-Chip (ORNoC): Architecture and design methodology
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
State-of-the-art System-on-Chip (SoC) consists of hundreds of processing elements, while trends in design of the next generation of SoC point to integration of thousand of processing elements, requiring high performance interconnect for high throughput communications. Optical on-chip interconnects are currently considered as one of the most promising paradigms for the design of such next generation Multi-Processors System on Chip (MPSoC). They enable significantly increased bandwidth, increased immunity to electromagnetic noise, decreased latency, and decreased power. Therefore, defining new architectures taking advantage of optical interconnects represents today a key issue for MPSoC designers. Moreover, new design methodologies, considering the design constraints specific to these architectures are mandatory. In this paper, we present a contention-free new architecture based on optical network on chip, called Optical Ring Network-on-Chip (ORNoC). We also show that our network scales well with both large 2D and 3D architectures. For the efficient design, we propose automatic wavelength-/waveguide assignment and demonstrate that the proposed architecture is capable of connecting 1296 nodes with only 102 waveguides and 64 wavelengths per waveguide.
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 | 0.000 | 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 | 0.000 | 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