Pruning and optimization of optical neural network as a binary optical trigger
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
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.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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