mlsanityData to 7 Oct 2026

Neural Network Quantization for Efficient Inference: A Survey

arXiv 2112.06126 ↗First posted 8 Dec 2021cs.LGcited by 13

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Abstract (arXiv)

As neural networks have become more powerful, there has been a rising desire to deploy them in the real world; however, the power and accuracy of neural networks is largely due to their depth and complexity, making them difficult to deploy, especially in resource-constrained devices. Neural network quantization has recently arisen to meet this demand of reducing the size and complexity of neural networks by reducing the precision of a network. With smaller and simpler networks, it becomes possible to run neural networks within the constraints of their target hardware. This paper surveys the many neural network quantization techniques that have been developed in the last decade. Based on this survey and comparison of neural network quantization techniques, we propose future directions of research in the area.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 28 of its 45 references that are in the map. First posted on arXiv 8 Dec 2021.

Builds on

28 of 45 references.

References · 28

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
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  3. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,773
  4. 2013Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computationcited by 2,031
  5. 2017Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inferencecited by 1,548
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  7. 2021A Survey of Quantization Methods for Efficient Neural Network Inferencecited by 798
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  12. 2018HAQ: Hardware-Aware Automated Quantization with Mixed Precisioncited by 426
16 more
  1. 2019HAWQ: Hessian AWare Quantization of Neural Networks with Mixed-Precisioncited by 387
  2. 2015Deep Learning with Limited Numerical Precisioncited by 368
  3. 2019HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networkscited by 250
  4. 2013Predicting Parameters in Deep Learningcited by 223
  5. 2016Trained Ternary Quantizationcited by 203
  6. 2016Ternary Weight Networkscited by 195
  7. 2013Maxout Networkscited by 186
  8. 2020Training with Quantization Noise for Extreme Model Compressioncited by 145
  9. 2015Fixed Point Quantization of Deep Convolutional Networkscited by 141
  10. 2017Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracycited by 100
  11. 2017Towards Effective Low-bitwidth Convolutional Neural Networkscited by 76
  12. 2020HAWQV3: Dyadic Neural Network Quantizationcited by 59
  13. 2021BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantizationcited by 37
  14. 2021Ps and Qs: Quantization-aware pruning for efficient low latency neural network inferencecited by 31
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  16. 2018Highly Efficient 8-bit Low Precision Inference of Convolutional Neural Networks with IntelCaffecited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 1; 2025: 5; 2026: 3.

13 papers.

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  2. 2022Reducing Computational Complexity of Neural Networks in Optical Channel Equalization: From Concepts to Implementationcited by 6
  3. 2025Efficient Deployment of Spiking Neural Networks on SpiNNaker2 for DVS Gesture Recognition Using Neuromorphic Intermediate Representationcited by 6
  4. 2023Model Compression Methods for YOLOv5: A Reviewcited by 2
  5. 2025Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devicescited by 1
  6. 2026How Controlling the Variance can Improve Training Stability of Sparsely Activated DNNs and CNNscited by 1
  7. 2023Neural Architecture Codesign for Fast Bragg Peak Analysiscited by 0
  8. 2024Artificial Neural Networks for Photonic Applications: From Algorithms to Implementationcited by 0
  9. 2025Automatic mixed precision for optimizing gained time with constrained loss mean-squared-error based on model partition to sequential sub-graphscited by 0
  10. 2025A Selective Quantization Tuner for ONNX Modelscited by 0
  11. 2025Vanishing Contributions: A Unified Framework for Smooth and Iterative Model Compressioncited by 0
  12. 2026SNAC-Pack 2.0: Scaled-Out Surrogate Neural Architecture Codesigncited by 0
1 more
  1. 2026Lyapunov-Guided Training for Hardware-Safe Neural Networks Under Fixed-Point Arithmeticcited by 0