BP-DBN
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 Toronto
- Organisation type
- Academia
- Country
- Canada
- Published
- 1 January 2009
- Authors
- Abdel-rahman Mohamed, George Dahl, and Geoffrey Hinton
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Speech
- Task
- Speech recognition (ASR)
- 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
- 18M
- Training data
- tokens
429*2048+2048*2048*4+2048*183=18030592 Table 2: largest model has 5 layers of 2048 into 183 softmax, 429 input
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA Tesla S1070
- Chips used
- 1
- Power draw
- 984 W
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- Historical significance,SOTA improvement
- Record confidence
- Confident
"On the standard TIMIT corpus, DBNs consistently outperform other techniques and the best DBN achieves a phone error rate (PER) of 23.0% on the TIMIT core test set."
Sources
Where this record came from and when it was last checked.
- Reference
- Deep Belief Networks for phone recognition
- Last updated
- 28 November 2025
What the numbers mean
About this model
BP-DBN was published by University of Toronto, in Canada, in January 2009. academia is the category the publisher falls under.
It works in Speech, and is recorded as doing speech recognition (ASR).
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
It is tracked in the underlying dataset for one reason in particular: historical significance,SOTA improvement.
Answers
BP-DBN — common questions
Who created BP-DBN?
BP-DBN was published by University of Toronto, based in Canada, categorised as academia.
When was BP-DBN released?
BP-DBN was published in January 2009. 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 BP-DBN used for?
BP-DBN works in Speech, and is recorded as handling speech recognition (ASR). These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run BP-DBN?
None. BP-DBN 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 BP-DBN open source?
No. BP-DBN has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does BP-DBN have?
BP-DBN has 18M parameters. 429*2048+2048*2048*4+2048*183=18030592 Table 2: largest model has 5 layers of 2048 into 183 softmax, 429 input. 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.
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.