GPU implementation of neural networks
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
- Soongsil University
- Organisation type
- Academia
- Country
- Korea (Republic of)
- Published
- 1 June 2004
- Authors
- KS Oh, K Jung
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Object detection
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.
- Training data
- tokens
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
- Citations
- 471
Sources
Where this record came from and when it was last checked.
- Reference
- GPU implementation of neural networks
- Last updated
- 28 November 2025
What the numbers mean
What this model is
GPU implementation of neural networks was published by Soongsil University, in the country recorded as Korea (Republic of), during June 2004. The publishing organisation is categorised as academia.
It works in the domain of Vision, and is recorded as performing the task of object detection.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
GPU implementation of neural networks — common questions
GPU implementation of neural networks— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
GPU implementation of neural networks— who created it?
It was published by Soongsil University, based in Korea (Republic of), an organisation categorised as academia.
GPU implementation of neural networks— when was it released?
It was published in June 2004. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
GPU implementation of neural networks— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of object detection. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
GPU implementation of neural networks— what GPU do I need to run it?
None. This 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.
GPU implementation of neural networks— is it open source?
The licensing 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.