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 Korea (Republic of), in June 2004. The organisation is categorised as academia.
It works in Vision, and is recorded as doing 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
How many parameters does GPU implementation of neural networks have?
No parameter count has been published for GPU implementation of neural networks, which is why no memory or speed figure appears on this page.
Who created GPU implementation of neural networks?
GPU implementation of neural networks was published by Soongsil University, based in Korea (Republic of), categorised as academia.
When was GPU implementation of neural networks released?
GPU implementation of neural networks 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.
What is GPU implementation of neural networks used for?
GPU implementation of neural networks works in Vision, and is recorded as handling object detection. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run GPU implementation of neural networks?
None. GPU implementation of neural networks 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 GPU implementation of neural networks open source?
The licensing for GPU implementation of neural networks 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.