# Benchmarking Quantized Neural Networks on FPGAs with FINN

arXiv: https://arxiv.org/abs/2102.01341 · HTML: https://mlsanity.com/paper/2102.01341/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 25 references that are in the map.
- First posted on arXiv 2 Feb 2021.

## Builds on (most cited first, 14 of 14)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks](https://mlsanity.com/paper/1603.05279/) (2016)
- [BinaryConnect: Training Deep Neural Networks with binary weights during propagations](https://mlsanity.com/paper/1511.00363/) (2015)
- [Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations](https://mlsanity.com/paper/1609.07061/) (2016)
- [Compressing Neural Networks with the Hashing Trick](https://mlsanity.com/paper/1504.04788/) (2015)
- [FINN: A Framework for Fast, Scalable Binarized Neural Network Inference](https://mlsanity.com/paper/1612.07119/) (2016)
- [FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks](https://mlsanity.com/paper/1809.04570/) (2018)
- [Streamlined Deployment for Quantized Neural Networks](https://mlsanity.com/paper/1709.04060/) (2017)
- [Neural Network Distiller: A Python Package For DNN Compression Research](https://mlsanity.com/paper/1910.12232/) (2019)
- [FINN-L: Library Extensions and Design Trade-off Analysis for Variable Precision LSTM Networks on FPGAs](https://mlsanity.com/paper/1807.04093/) (2018)
- [Quantisation and Pruning for Neural Network Compression and Regularisation](https://mlsanity.com/paper/2001.04850/) (2020)
- [Training Competitive Binary Neural Networks from Scratch](https://mlsanity.com/paper/1812.01965/) (2018)
- [QuTiBench: Benchmarking Neural Networks on Heterogeneous Hardware](https://mlsanity.com/paper/1909.05009/) (2019)

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

- [Computer Vision Model Compression Techniques for Embedded Systems: A Survey](https://mlsanity.com/paper/2408.08250/) (2024)
- ["bot lane noob" Towards Deployment of NLP-based Toxicity Detectors in Video Games](https://mlsanity.com/paper/2604.10175/) (2026)

## Abstract (arXiv)

The ever-growing cost of both training and inference for state-of-the-art neural networks has brought literature to look upon ways to cut off resources used with a minimal impact on accuracy. Using lower precision comes at the cost of negligible loss in accuracy. While training neural networks may require a powerful setup, deploying a network must be possible on low-power and low-resource hardware architectures. Reconfigurable architectures have proven to be more powerful and flexible than GPUs when looking at a specific application. This article aims to assess the impact of mixed-precision when applied to neural networks deployed on FPGAs. While several frameworks exist that create tools to deploy neural networks using reduced-precision, few of them assess the importance of quantization and the framework quality. FINN and Brevitas, two frameworks from Xilinx labs, are used to assess the impact of quantization on neural networks using 2 to 8 bit precisions and weights with several parallelization configurations. Equivalent accuracy can be obtained using lower-precision representation and enough training. However, the compressed network can be better parallelized allowing the deployed network throughput to be 62 times faster. The benchmark set up in this work is available in a public repository (https://github.com/QDucasse/nn benchmark).