Pruning and optimization of optical neural network as a binary optical trigger
Bibliographic record
Abstract
Optical neural networks implemented with Mach-Zehnder Interferometer (MZI) arrays are a promising solution to enable fast and energy-efficient machine learning inference, yet finding a practical application has proven challenging due to sensitivity to thermal noise and loss. To leverage the distinct advantages of integrated optical processors while avoiding its shortcomings given the current state of optical computing, we propose the binary optical trigger as a promising field of application. Implementable as small-scale application-specific circuitry on edge devices, the binary trigger runs binary classification tasks and output binary signals to decide if a subsequent energy intensive system should activate. Motivated by the limited task complexity, constrained area and power budgets of binary triggers, we perform 1) systematic, application-specific hardware pruning by physically removing specific MZIs, and 2) application-specific optimizations in the form of false negative reduction and weight quantization, as well as 3) sensitivity studies capturing the effect of imperfections in real optical components. The result is a customized MZI-mesh topology, MiniBokun Mesh, whose structure provides adequate performance and robustness for a targeted task complexity. We demonstrate in simulation that the pruning methodology achieves at least 50% less MZI usage compared to Clements and Reck meshes with the same input size, translating to at least between 4.6% and 24.2% savings in power consumption and a 40% reduction in physical circuitry footprint compared to other proposed unitary MZI topologies, sacrificing only 1%–2% drop in inference accuracy.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".