# A Survey on Methods and Theories of Quantized Neural Networks

arXiv: https://arxiv.org/abs/1808.04752 · HTML: https://mlsanity.com/paper/1808.04752/

## Key facts

- Cited by 75 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2018.

## Built on by (most cited first, 30 of 75)

- [Pruning and Quantization for Deep Neural Network Acceleration: A Survey](https://mlsanity.com/paper/2101.09671/) (2021)
- [Structured Pruning for Deep Convolutional Neural Networks: A survey](https://mlsanity.com/paper/2303.00566/) (2023)
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- [Towards Mixed-Precision Quantization of Neural Networks via Constrained Optimization](https://mlsanity.com/paper/2110.06554/) (2021)
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- [Post-training Quantization for Neural Networks with Provable Guarantees](https://mlsanity.com/paper/2201.11113/) (2022)
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- [LLaMAntino: LLaMA 2 Models for Effective Text Generation in Italian Language](https://mlsanity.com/paper/2312.09993/) (2023)
- [On the Adversarial Robustness of Quantized Neural Networks](https://mlsanity.com/paper/2105.00227/) (2021)
- [Demystifying and Generalizing BinaryConnect](https://mlsanity.com/paper/2110.13220/) (2021)
- [AdaSTE: An Adaptive Straight-Through Estimator to Train Binary Neural Networks](https://mlsanity.com/paper/2112.02880/) (2021)
- [Q-S5: Towards Quantized State Space Models](https://mlsanity.com/paper/2406.09477/) (2024)
- [Dynamic Encoder Transducer: A Flexible Solution For Trading Off Accuracy For Latency](https://mlsanity.com/paper/2104.02176/) (2021)
- [Efficient Neural Networks for Tiny Machine Learning: A Comprehensive Review](https://mlsanity.com/paper/2311.11883/) (2023)
- [A simple approach for quantizing neural networks](https://mlsanity.com/paper/2209.03487/) (2022)
- [Fixed-point Quantization of Convolutional Neural Networks for Quantized Inference on Embedded Platforms](https://mlsanity.com/paper/2102.02147/) (2021)
- [CoCoFL: Communication- and Computation-Aware Federated Learning via Partial NN Freezing and Quantization](https://mlsanity.com/paper/2203.05468/) (2022)
- [Mixed-Precision Neural Networks: A Survey](https://mlsanity.com/paper/2208.06064/) (2022)
- [A quantitative analysis of semantic information in deep representations of text and images](https://mlsanity.com/paper/2505.17101/) (2025)
- [Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators](https://mlsanity.com/paper/2104.08323/) (2021)
- [Verifying Quantized Neural Networks using SMT-Based Model Checking](https://mlsanity.com/paper/2106.05997/) (2021)
- [Survey: Exploiting Data Redundancy for Optimization of Deep Learning](https://mlsanity.com/paper/2208.13363/) (2022)
- [Personalized Federated Learning with Communication Compression](https://mlsanity.com/paper/2209.05148/) (2022)
- [Using Neighborhood Context to Improve Information Extraction from Visual Documents Captured on Mobile Phones](https://mlsanity.com/paper/2108.10395/) (2021)
- [Approximation of functions with one-bit neural networks](https://mlsanity.com/paper/2112.09181/) (2021)
- [Towards Efficient In-memory Computing Hardware for Quantized Neural Networks: State-of-the-art, Open Challenges and Perspectives](https://mlsanity.com/paper/2307.03936/) (2023)
- [Multiplierless MP-Kernel Machine For Energy-efficient Edge Devices](https://mlsanity.com/paper/2106.01958/) (2021)
- [QNNVerifier: A Tool for Verifying Neural Networks using SMT-Based Model Checking](https://mlsanity.com/paper/2111.13110/) (2021)

## Abstract (arXiv)

Deep neural networks are the state-of-the-art methods for many real-world tasks, such as computer vision, natural language processing and speech recognition. For all its popularity, deep neural networks are also criticized for consuming a lot of memory and draining battery life of devices during training and inference. This makes it hard to deploy these models on mobile or embedded devices which have tight resource constraints. Quantization is recognized as one of the most effective approaches to satisfy the extreme memory requirements that deep neural network models demand. Instead of adopting 32-bit floating point format to represent weights, quantized representations store weights using more compact formats such as integers or even binary numbers. Despite a possible degradation in predictive performance, quantization provides a potential solution to greatly reduce the model size and the energy consumption. In this survey, we give a thorough review of different aspects of quantized neural networks. Current challenges and trends of quantized neural networks are also discussed.