Large-Scale Optical Neural Networks Based on Photoelectric Multiplication
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
No Canadian affiliation. An affiliation-only frame — the usual design — would never have seen this work. It is one of the works that make the case for inverting the frame.
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.258 · how far apart the two teachers sit on this one work
- Validation status
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
Abstract
A scheme for implementing optical neural networks offers the energy benefits of optical components while being scalable to large systems, promising low-energy processing with order-of-magnitude improvements in network performance.
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.
The record
- Venue
- Physical Review X
- Topic
- Neural Networks and Reservoir Computing
- Field
- Computer Science
- Canadian institutions
- —
- Funders
- Army Research OfficeNatural Sciences and Engineering Research Council of CanadaNvidiaOak Ridge Institute for Science and EducationOffice of the Director of National IntelligenceU.S. Department of Energy
- Keywords
- Computer scienceScalabilityConvolutional neural networkArtificial neural networkPhotonicsLimit (mathematics)Deep learningNoise (video)Hardware accelerationPhysicsComputer engineeringComputer hardwareArtificial intelligenceOpticsField-programmable gate arrayImage (mathematics)
- Has abstract in OpenAlex
- yes