# Ternary Quantization: A Survey

arXiv: https://arxiv.org/abs/2303.01505 · HTML: https://mlsanity.com/paper/2303.01505/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 65 references that are in the map.
- First posted on arXiv 2 Mar 2023.

## Builds on (most cited first, 30 of 41)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [A Survey of Quantization Methods for Efficient Neural Network Inference](https://mlsanity.com/paper/2103.13630/) (2021)
- [BinaryConnect: Training Deep Neural Networks with binary weights during propagations](https://mlsanity.com/paper/1511.00363/) (2015)
- [Variational Dropout and the Local Reparameterization Trick](https://mlsanity.com/paper/1506.02557/) (2015)
- [Deep Learning with Limited Numerical Precision](https://mlsanity.com/paper/1502.02551/) (2015)
- [Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks](https://mlsanity.com/paper/1908.05033/) (2019)
- [Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights](https://mlsanity.com/paper/1702.03044/) (2017)
- [Trained Ternary Quantization](https://mlsanity.com/paper/1612.01064/) (2016)
- [Ternary Weight Networks](https://mlsanity.com/paper/1605.04711/) (2016)
- [Training with Quantization Noise for Extreme Model Compression](https://mlsanity.com/paper/2004.07320/) (2020)
- [Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy](https://mlsanity.com/paper/1711.05852/) (2017)
- [Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing](https://mlsanity.com/paper/1603.08270/) (2016)
- [Extremely Low Bit Neural Network: Squeeze the Last Bit Out with ADMM](https://mlsanity.com/paper/1707.09870/) (2017)
- [ProxQuant: Quantized Neural Networks via Proximal Operators](https://mlsanity.com/paper/1810.00861/) (2018)
- [WRPN: Wide Reduced-Precision Networks](https://mlsanity.com/paper/1709.01134/) (2017)
- [Neural Networks with Few Multiplications](https://mlsanity.com/paper/1510.03009/) (2015)
- [Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML](https://mlsanity.com/paper/2003.06308/) (2020)
- [Resiliency of Deep Neural Networks under Quantization](https://mlsanity.com/paper/1511.06488/) (2015)
- [Ternary Neural Networks for Resource-Efficient AI Applications](https://mlsanity.com/paper/1609.00222/) (2016)
- [Loss-aware Weight Quantization of Deep Networks](https://mlsanity.com/paper/1802.08635/) (2018)
- [Learning Discrete Weights Using the Local Reparameterization Trick](https://mlsanity.com/paper/1710.07739/) (2017)
- [SYQ: Learning Symmetric Quantization For Efficient Deep Neural Networks](https://mlsanity.com/paper/1807.00301/) (2018)
- [BinaryDuo: Reducing Gradient Mismatch in Binary Activation Network by Coupling Binary Activations](https://mlsanity.com/paper/2002.06517/) (2020)
- [Ternary Neural Networks with Fine-Grained Quantization](https://mlsanity.com/paper/1705.01462/) (2017)
- [RTN: Reparameterized Ternary Network](https://mlsanity.com/paper/1912.02057/) (2019)
- [Quantization and Training of Low Bit-Width Convolutional Neural Networks for Object Detection](https://mlsanity.com/paper/1612.06052/) (2016)
- [Adaptive Binary-Ternary Quantization](https://mlsanity.com/paper/1909.12205/) (2019)

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

- [Tequila: Trapping-free Ternary Quantization for Large Language Models](https://mlsanity.com/paper/2509.23809/) (2025)
- [Big2Small: A Unifying Neural Network Framework for Model Compression](https://mlsanity.com/paper/2603.29768/) (2026)

## Abstract (arXiv)

Inference time, model size, and accuracy are critical for deploying deep neural network models. Numerous research efforts have been made to compress neural network models with faster inference and higher accuracy. Pruning and quantization are mainstream methods to this end. During model quantization, converting individual float values of layer weights to low-precision ones can substantially reduce the computational overhead and improve the inference speed. Many quantization methods have been studied, for example, vector quantization, low-bit quantization, and binary/ternary quantization. This survey focuses on ternary quantization. We review the evolution of ternary quantization and investigate the relationships among existing ternary quantization methods from the perspective of projection function and optimization methods.