# BCNN: A Binary CNN with All Matrix Ops Quantized to 1 Bit Precision

arXiv: https://arxiv.org/abs/2010.00704 · HTML: https://mlsanity.com/paper/2010.00704/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2020.

## Built on by (most cited first, 6 of 6)

- [A comprehensive review of Binary Neural Network](https://mlsanity.com/paper/2110.06804/) (2021)
- [Resource-Efficient Deep Learning: A Survey on Model-, Arithmetic-, and Implementation-Level Techniques](https://mlsanity.com/paper/2112.15131/) (2021)
- [PROM: Prioritize Reduction of Multiplications Over Lower Bit-Widths for Efficient CNNs](https://mlsanity.com/paper/2505.03254/) (2025)
- [How to train accurate BNNs for embedded systems?](https://mlsanity.com/paper/2206.12322/) (2022)
- [NAS-BNN: Neural Architecture Search for Binary Neural Networks](https://mlsanity.com/paper/2408.15484/) (2024)
- [Binarized High-Efficiency RAW Video Restoration and Beyond](https://mlsanity.com/paper/2608.16756/) (2026)

## Abstract (arXiv)

This paper describes a CNN where all CNN style 2D convolution operations that lower to matrix matrix multiplication are fully binary. The network is derived from a common building block structure that is consistent with a constructive proof outline showing that binary neural networks are universal function approximators. 71.24% top 1 accuracy on the 2012 ImageNet validation set was achieved with a 2 step training procedure and implementation strategies optimized for binary operands are provided.