# Development of Quantized DNN Library for Exact Hardware Emulation

arXiv: https://arxiv.org/abs/2106.08892 · HTML: https://mlsanity.com/paper/2106.08892/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 22 references that are in the map.
- First posted on arXiv 15 Jun 2021.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients](https://mlsanity.com/paper/1606.06160/) (2016)
- [Empirical Evaluation of Rectified Activations in Convolutional Network](https://mlsanity.com/paper/1505.00853/) (2015)
- [A Survey of Model Compression and Acceleration for Deep Neural Networks](https://mlsanity.com/paper/1710.09282/) (2017)
- [QPyTorch: A Low-Precision Arithmetic Simulation Framework](https://mlsanity.com/paper/1910.04540/) (2019)
- [TensorQuant - A Simulation Toolbox for Deep Neural Network Quantization](https://mlsanity.com/paper/1710.05758/) (2017)

## Abstract (arXiv)

Quantization is used to speed up execution time and save power when runnning Deep neural networks (DNNs) on edge devices like AI chips. To investigate the effect of quantization, we need performing inference after quantizing the weights of DNN with 32-bit floating-point precision by a some bit width, and then quantizing them back to 32-bit floating-point precision. This is because the DNN library can only handle floating-point numbers. However, the accuracy of the emulation does not provide accurate precision. We need accurate precision to detect overflow in MAC operations or to verify the operation on edge de vices. We have developed PyParch, a DNN library that executes quantized DNNs (QNNs) with exactly the same be havior as hardware. In this paper, we describe a new proposal and implementation of PyParch. As a result of the evaluation, the accuracy of QNNs with arbitrary bit widths can be estimated for la rge and complex DNNs such as YOLOv5, and the overflow can be detected. We evaluated the overhead of the emulation time and found that it was 5.6 times slower for QNN and 42 times slower for QNN with overflow detection compared to the normal DNN execution time.