Awesome

convnet-benchmarks

Easy benchmarking of all public open-source implementations of convnets. A summary is provided in the section below.

Machine: 6-core Intel Core i7-5930K CPU @ 3.50GHz + NVIDIA Titan X + Ubuntu 14.04 x86_64

Imagenet Winners Benchmarking

I pick some popular imagenet models, and I clock the time for a full forward + backward pass. I average my times over 10 runs. I ignored dropout and softmax layers.

Notation

Input is described as {batch_size}x{num_filters}x{filter_width}x{filter_height}. Where batch_size is the number of images used in a minibatch, num_filters is the number of channels in an image, filter_width is the width of the image, and filter_height is the height of the image.

One small note:

The CuDNN benchmarks are done using Torch bindings. One can also do the same via Caffe bindings or bindings of any other library. This note is here to clarify that Caffe (native) and Torch (native) are the convolution kernels which are present as a default fallback. Some of the frameworks like TensorFlow and Chainer are benchmarked with CuDNN, but it is not explicitly mentioned, and hence one might think that these frameworks as a whole are faster, than for example Caffe, which might not be the case.

AlexNet (One Weird Trick paper) - Input 128x3x224x224

Overfeat [fast] - Input 128x3x231x231

OxfordNet [Model-A] - Input 64x3x224x224

GoogleNet V1 - Input 128x3x224x224

Layer-wise Benchmarking (Last Updated April 2015)

Spatial Convolution layer (3D input 3D output, densely connected)

forward + backprop (wrt input and weights)

This table is NOT UPDATED For TITAN-X. These numbers below were on Titan Black and are here only for informational and legacy purposes.

• * indicates that the library was tested with Torch bindings of the specific kernels.
• ** indicates that the library was tested with Pylearn2 bindings.
• *** This is an experimental module which used FFT to calculate convolutions. It uses a lot of memory according to @benanne
• **** The last row shows results obtainable when choosing the best-performing library for each layer.
• L1 - Input: 128x128 Batch-size 128, Feature maps: 3->96, Kernel Size: 11x11, Stride: 1x1
• L2 - Input: 64x64 Batch-size 128, Feature maps: 64->128, Kernel Size: 9x9, Stride: 1x1
• L3 - Input: 32x32 Batch-size 128, Feature maps: 128->128, Kernel Size: 9x9, Stride: 1x1
• L4 - Input: 16x16 Batch-size 128, Feature maps: 128->128, Kernel Size: 7x7, Stride: 1x1
• L5 - Input: 13x13 Batch-size 128, Feature maps: 384->384, Kernel Size: 3x3, Stride: 1x1
• The table is ranked according to the total time forward+backward calls for layers (L1 + L2 + L3 + L4 + L5)

Breakdown

forward

Columns L1, L2, L3, L4, L5, Total are times in milliseconds

backward (gradInput + gradWeight)

Columns L1, L2, L3, L4, L5, Total are times in milliseconds