  Nowadays Deep Learning became widely used in many economic, technical and
scientific areas of human interest. It is clear that efficiency of solutions
based on Deep Neural Networks should consider not only quality metric for the
target task, but also latency and constraints of target platform design should
be taken into account. In this paper we present novel hardware-friendly
Tensor-Train decomposition implementation for Convolutional Neural Networks
together with Tensor Yard - one-shot training algorithm which optimizes an
order of decomposition of network layers. These ideas allow to accelerate
ResNet models on Ascend 310 NPU devices without significant loss of accuracy.
For example we accelerate ResNet-101 by 14.6% with drop by 0.5 of top-1
ImageNet accuracy.
