# Fast Exploration of Weight Sharing Opportunities for CNN Compression

arXiv: https://arxiv.org/abs/2102.01345 · HTML: https://mlsanity.com/paper/2102.01345/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 7 references that are in the map.
- First posted on arXiv 2 Feb 2021.

## Builds on (most cited first, 5 of 5)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [Efficient Processing of Deep Neural Networks: A Tutorial and Survey](https://mlsanity.com/paper/1703.09039/) (2017)
- [Online Embedding Compression for Text Classification using Low Rank Matrix Factorization](https://mlsanity.com/paper/1811.00641/) (2018)
- [UNIQ: Uniform Noise Injection for Non-Uniform Quantization of Neural Networks](https://mlsanity.com/paper/1804.10969/) (2018)

## Abstract (arXiv)

The computational workload involved in Convolutional Neural Networks (CNNs) is typically out of reach for low-power embedded devices. There are a large number of approximation techniques to address this problem. These methods have hyper-parameters that need to be optimized for each CNNs using design space exploration (DSE). The goal of this work is to demonstrate that the DSE phase time can easily explode for state of the art CNN. We thus propose the use of an optimized exploration process to drastically reduce the exploration time without sacrificing the quality of the output.