Photonic integrated field-programmable disk array signal processor
Bibliographic record
Abstract
Thanks to the nature of strong programmability, field-programmable gate arrays (FPGAs) have been playing a significant role in signal processing and control. With the explosive growth in digital data, big data analytics becomes an important emerging field, in which FPGAs are a major player. However, the computational speed and power efficiency provided by FPGAs are limited by electronic clock rates and Ohmic losses. To overcome the limitations, photonics is envisioned as an enabling solution, thanks to its ultrafast and low power consumption feature. In this paper, we propose a scalable photonic field-programmable disk array (FPDA) signal processor. Ultra-compact microdisk resonators are leveraged as a fundamental execution units in the core to route, store and process optical signals. By field-programming the processor, diverse circuit topologies can be realized to perform multiple specific signal processing functions including filtering, temporal differentiation, time delay, beamforming, and spectral shaping.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".