Blitz is a cross platform library for deep learning computations. It aims to provide simple, portable, and efficient interfaces for neural network developers. Unlike deep learning frameworks such as Caffe, Torch, and Tensorflow, it could be quickly deployed on different platforms separately. And thus users could fast instanitiate and extend neural network computations with high performance.

• linux systems
• boost
• glog

• x86-64 processors
• g++/icc
• openblas/atlas/mkl

• NVIDIA GPUs

• yaml-cpp

For convolution networks, we support NCHW and NHWC data layouts for the convolution and pooling computations in forward, backward and update phases. Besides, we devise a feature for automatic data transformation. It is designed for tunning the performance on the network level. For instance, if some layers performs well on NCHW layouts while others are best for NHWC layouts, we could specify the output formats of the convolution layer. The internal transformation does not require extra copies.

Our GPU backend supports NCHW, NHWC, and CHWN data layouts. Currently we use CUBLAS for the first two layouts, and the CHWN layouts is fir for our assembly implementations on Kepler GPU, which outperforms CUDNN's GEMM algorithm in several configurations.

A convenient feature of blitz is that CPU, GPU and MIC share common interfaces. We use a contiguous tensor to manage data on different devices, and a template to instanitiate algorithms. A more compherensive guide is presented as following.

• Convolution

make AVX=0/1/2/3 BLAS=openblas/atlas/mkl

make USE_GPU=1 CUDA_ARCH=<according to specific GPU generation>

make AVX=512