The emerging edge computing has promoted immense interests in compacting a neural network without sacrificing much accuracy. In this regard, low-rank tensor decomposition constitutes a powerful tool to compress convolutional neural networks (CNNs) by decomposing the 4-way kernel tensor into multi-stage smaller ones. Building on top of Tucker-2 decomposition, we propose a generalized Higher Order Tucker Articulated Kernels (HOTCAKE) scheme comprising four steps: input channel decomposition, guided Tucker rank selection, higher order Tucker decomposition and fine-tuning. By subjecting each CONV layer to HOTCAKE, a highly compressed CNN model with graceful accuracy trade-off is obtained. Experiments show HOTCAKE can compress even pre-compressed models and produce state-of-the-art lightweight networks.
@article{arxiv.2002.12663,
title = {HOTCAKE: Higher Order Tucker Articulated Kernels for Deeper CNN Compression},
author = {Rui Lin and Ching-Yun Ko and Zhuolun He and Cong Chen and Yuan Cheng and Hao Yu and Graziano Chesi and Ngai Wong},
journal= {arXiv preprint arXiv:2002.12663},
year = {2020}
}