Diffractive Deep Neural Network
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- University of California Los Angeles (UCLA)
- Organisation type
- Academia
- Country
- United States of America
- Published
- 14 April 2018
- Authors
- Xing Lin, Yair Rivenson, Nezih T Yardimci, Muhammed Veli, Yi Luo, Mona Jarrahi, and Aydogan Ozcan
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Digit recognition
- Approach
- Supervised
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Parameters
- 8B
- Training data
- 55,000 tokens
"For example, using five 3D-printed transmission layers, containing a total of 0.2 million neurons and ~8.0 billion connections that are trained using deep learning, we experimentally demonstrated the function of a handwritten digit classifier." My understanding is that every connection correspond to the parameter to learn.
size of MNIST "For this task, phase-only transmission masks were designed by training a 5-layer D2NN with ~55,000 images from MNIST handwritten digit database (14). "
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
- Citations
- 2,190
Sources
Where this record came from and when it was last checked.
- Reference
- All-Optical Machine Learning Using Diffractive Deep Neural Networks
- Last updated
- 25 May 2026
What the numbers mean
What this model is
Diffractive Deep Neural Network was published by University of California Los Angeles (UCLA), in United States of America, in April 2018. It comes out of academia.
It works in Vision, and is recorded as doing digit recognition.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
It was trained on about 55,000 tokens of text.
Answers
Diffractive Deep Neural Network — common questions
How many parameters does Diffractive Deep Neural Network have?
Diffractive Deep Neural Network has 8B parameters. "For example, using five 3D-printed transmission layers, containing a total of 0.2 million neurons and ~8.0 billion connections that are trained using deep learning, we experimentally demonstrated the function of a handwritten digit classifier." My understanding is that every connection correspond to the parameter to learn. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Who created Diffractive Deep Neural Network?
Diffractive Deep Neural Network was published by University of California Los Angeles (UCLA), based in United States of America, categorised as academia.
When was Diffractive Deep Neural Network released?
Diffractive Deep Neural Network was published in April 2018. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is Diffractive Deep Neural Network used for?
Diffractive Deep Neural Network works in Vision, and is recorded as handling digit recognition. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run Diffractive Deep Neural Network?
None. Diffractive Deep Neural Network is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is Diffractive Deep Neural Network open source?
The licensing for Diffractive Deep Neural Network was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.