ConvNet Processor
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
- Courant Institute of Mathematical Sciences
- Organisation type
- Academia
- Country
- United States of America
- Published
- 31 August 2009
- Authors
- Clément Farabet, Cyril Poulet, Jefferson Y Han, Yann LeCun
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Face 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.
- Parameters
- 14.4K
- Training data
- 30,000 tokens
- Epochs
- 5
C1: 6x7x7 C3: 61x7x7 C5: 305x6x6 F6: 80x2 Total = 14423
"The dataset contained 45,000 images from various sources, of which 30,000 were used for training, and 15,000 for testing"
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
- 3.1 × 10¹⁴ FLOP
- How it was established
- Operation counting
(2*340 million) * 3 * 30000 * 5 "340 million connection network" operations per fwd pass ~= 2*340 million "With a fixed detection threshold, the system reaches a roughly 3% equal error rate on this dataset after only 5 training epochs through the training set."
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
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
Sources
Where this record came from and when it was last checked.
- Reference
- CNP: An FPGA-based processor for convolutional networks
- Last updated
- 28 November 2025
What the numbers mean
About this model
ConvNet Processor was published by Courant Institute of Mathematical Sciences, in the country recorded as United States of America, during August 2009. The category the publisher falls under is academia.
It works in the domain of Vision, and is recorded as performing the task of face detection.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
Producing it required arithmetic totalling around 3.1 × 10¹⁴ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
It was trained on a corpus of about 30,000 tokens of text.
Answers
ConvNet Processor — common questions
ConvNet Processor— how many parameters does it have?
It has a parameter count of 14.4K. C1: 6x7x7 C3: 61x7x7 C5: 305x6x6 F6: 80x2 Total = 14423. 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.
ConvNet Processor— who created it?
It was published by Courant Institute of Mathematical Sciences, based in United States of America, an organisation categorised as academia.
ConvNet Processor— when was it released?
It was published in August 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.
ConvNet Processor— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of face detection. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
ConvNet Processor— how much compute was used to train it?
Training consumed around 3.1 × 10¹⁴ 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.
ConvNet Processor— 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.
ConvNet Processor— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
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.