# Hardware-friendly Deep Learning by Network Quantization and Binarization

arXiv: https://arxiv.org/abs/2112.00737 · HTML: https://mlsanity.com/paper/2112.00737/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 6 references that are in the map.
- First posted on arXiv 1 Dec 2021.

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

- [Binary Neural Networks: A Survey](https://mlsanity.com/paper/2004.03333/) (2020)
- [Forward and Backward Information Retention for Accurate Binary Neural Networks](https://mlsanity.com/paper/1909.10788/) (2019)
- [Diversifying Sample Generation for Accurate Data-Free Quantization](https://mlsanity.com/paper/2103.01049/) (2021)
- [BiPointNet: Binary Neural Network for Point Clouds](https://mlsanity.com/paper/2010.05501/) (2020)
- [PhoneBit: Efficient GPU-Accelerated Binary Neural Network Inference Engine for Mobile Phones](https://mlsanity.com/paper/1912.04050/) (2019)

## Built on by (most cited first, 2 of 2)

- [BiBench: Benchmarking and Analyzing Network Binarization](https://mlsanity.com/paper/2301.11233/) (2023)
- [From Arithmetic to Logic: The Resilience of Logic and Lookup-Based Neural Networks Under Parameter Bit-Flips](https://mlsanity.com/paper/2603.22770/) (2026)

## Abstract (arXiv)

Quantization is emerging as an efficient approach to promote hardware-friendly deep learning and run deep neural networks on resource-limited hardware. However, it still causes a significant decrease to the network in accuracy. We summarize challenges of quantization into two categories: Quantization for Diverse Architectures and Quantization on Complex Scenes. Our studies focus mainly on applying quantization on various architectures and scenes and pushing the limit of quantization to extremely compress and accelerate networks. The comprehensive research on quantization will achieve more powerful, more efficient, and more flexible hardware-friendly deep learning, and make it better suited to more real-world applications.