MCDNN (MNIST)
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
- IDSIA,SUPSI
- Organisation type
- Academia,Academia
- Country
- Switzerland
- Published
- 13 February 2012
- Authors
- Dan Cireşan, Ueli Meier, Juergen Schmidhuber
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Character recognition (OCR), Image classification
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
- 2.7M
- Training data
- 2,100,000 tokens
- Epochs
- 800
"We train five DNN columns per normalization, resulting in a total of 35 columns for the entire MCDNN. [Each DNN has an architecture] 1x29x29-20C4-MP2-40C5-MP3-150N-10N DNN" Parameter calculation: 20*4*4+20*40*5*5+150*40*3*3+10*150=75820 35*75820=2653700
The MNIST database contains 60,000 training images and 10,000 testing images (Wikipedia)
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 1.6 × 10¹⁶ FLOP
- How it was established
- Operation counting
Num of multiply-adds per forward pass 2 FLOPs/mult-add 3 (fp+bp FLOPs / fp FLOPs) 800 epochs 60.000 training size 35 networks "Training a DNN takes almost 14 hours and after 500 training epochs little additional improvement is observed" 500*60000*3*174147400=1.57e+16
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.
- Record confidence
- Likely
- Citations
- 4,828
Sources
Where this record came from and when it was last checked.
- Reference
- Multi-column Deep Neural Networks for Image Classification
- Last updated
- 11 February 2026
What the numbers mean
Background
MCDNN (MNIST) was published by IDSIA,SUPSI, in Switzerland, in February 2012. It comes out of academia,Academia.
It works in Vision, and is recorded as doing character recognition (OCR), Image classification.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
Producing it required around 1.6 × 10¹⁶ FLOP of arithmetic, which is a statement about the training budget rather than about inference.
Around 2,100,000 tokens went into training it.
Answers
MCDNN (MNIST) — common questions
Is MCDNN (MNIST) open source?
No. MCDNN (MNIST) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does MCDNN (MNIST) have?
MCDNN (MNIST) has 2.7M parameters. "We train five DNN columns per normalization, resulting in a total of 35 columns for the entire MCDNN. [Each DNN has an architecture] 1x29x29-20C4-MP2-40C5-MP3-150N-10N DNN" Parameter calculation: 20*4*4+20*40*5*5+150*40*3*3+10*150=75820 35*75820=2653700. 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 MCDNN (MNIST)?
MCDNN (MNIST) was published by IDSIA,SUPSI, based in Switzerland, categorised as academia,Academia.
When was MCDNN (MNIST) released?
MCDNN (MNIST) was published in February 2012. 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 MCDNN (MNIST) used for?
MCDNN (MNIST) works in Vision, and is recorded as handling character recognition (OCR), Image classification. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
How much compute was used to train MCDNN (MNIST)?
Around 1.6 × 10¹⁶ FLOP. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
What GPU do I need to run MCDNN (MNIST)?
None. MCDNN (MNIST) 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.
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.