Nanophotonic Cavity Based Synapse for Scalable Photonic Neural Networks
Bibliographic record
Abstract
The bandwidth and energy demands of neural networks has spurred tremendous interest in developing novel neuromorphic hardware, including photonic integrated circuits. Although an optical waveguide can accommodate hundreds of channels with THz bandwidth, the channel count of photonic systems is always bottlenecked by the devices within. In WDM-based photonic neural networks, the synapses, i.e. network interconnections, are typically realized by microring resonators (MRRs), where the WDM channel count ($N$) is bounded by the free-spectral range of the MRRs. For typical Si MRRs, we estimate$N \leq 30$within the C-band. This not only restrains the aggregate throughput of the neural network but also makes applications with high input dimensions unfeasible. We experimentally demonstrate that photonic crystal nanobeam based synapses can be FSR-free within C-band, eliminating the bound on channel count. This increases data throughput as well as enables applications with high-dimensional inputs like natural language processing and high resolution image processing. In addition, the smaller physical footprint of photonic crystal nanobeam cavities offers higher tuning energy efficiency and a higher compute density than MRRs. Nanophotonic cavity based synapse thus offers a path towards realizing highly scalable photonic neural networks.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".