# HOTCAKE: Higher Order Tucker Articulated Kernels for Deeper CNN Compression

arXiv: https://arxiv.org/abs/2002.12663 · HTML: https://mlsanity.com/paper/2002.12663/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2020.

## Built on by (most cited first, 3 of 3)

- [ReLoRA: High-Rank Training Through Low-Rank Updates](https://mlsanity.com/paper/2307.05695/) (2023)
- [ELRT: Efficient Low-Rank Training for Compact Convolutional Neural Networks](https://mlsanity.com/paper/2401.10341/) (2024)
- [Scalable Efficient Training of Large Language Models with Low-dimensional Projected Attention](https://mlsanity.com/paper/2411.02063/) (2024)

## Abstract (arXiv)

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.