  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.
