CNN Acceleration by Low-rank Approximation with Quantized Factors

Understand

The modern convolutional neural networks although achieve great results in solving complex computer vision tasks still cannot be effectively used in mobile and embedded devices due to the strict requirements for computational complexity, memory and power consumption.

• The CNNs have to be compressed and accelerated before deployment.
• In order to solve this problem the novel approach combining two known methods, low-rank tensor approximation in Tucker format and quantization of weights and feature maps (activations), is proposed.
• The greedy one-step and multi-step algorithms for the task of multilinear rank selection are proposed.