Data to 14 Oct 2026

Quantized Convolutional Neural Networks for Mobile Devices

arXiv 1512.06473 ↗First posted 21 Dec 2015cs.CVcited by 215

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Cited by 39 in Knowledge distillation, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Recently, convolutional neural networks (CNN) have demonstrated impressive performance in various computer vision tasks. However, high performance hardware is typically indispensable for the application of CNN models due to the high computation complexity, which prohibits their further extensions. In this paper, we propose an efficient framework, namely Quantized CNN, to simultaneously speed-up the computation and reduce the storage and memory overhead of CNN models. Both filter kernels in convolutional layers and weighting matrices in fully-connected layers are quantized, aiming at minimizing the estimation error of each layer's response. Extensive experiments on the ILSVRC-12 benchmark demonstrate 4~6x speed-up and 15~20x compression with merely one percentage loss of classification accuracy. With our quantized CNN model, even mobile devices can accurately classify images within one second.

Key facts. Cited by 215 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Dec 2015.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 66; 2022: 45; 2023: 47; 2024: 32; 2025: 19; 2026: 6.

215 papers.

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2021A Survey of Quantization Methods for Efficient Neural Network Inferencecited by 815
2023SqueezeLLM: Dense-and-Sparse Quantizationcited by 294
2021Pruning and Quantization for Deep Neural Network Acceleration: A Surveycited by 263
2021I-BERT: Integer-only BERT Quantizationcited by 255
2023DepGraph: Towards Any Structural Pruningcited by 203
2022Cross-Image Relational Knowledge Distillation for Semantic Segmentationcited by 118
2021Distilling Object Detectors via Decoupled Featurescited by 106
2023DataDAM: Efficient Dataset Distillation with Attention Matchingcited by 84
2024GPTVQ: The Blessing of Dimensionality for LLM Quantizationcited by 82
2021VS-Quant: Per-vector Scaled Quantization for Accurate Low-Precision Neural Network Inferencecited by 64
2021hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devicescited by 62
2021Pufferfish: Communication-efficient Models At No Extra Costcited by 61

38 more

2021Online Knowledge Distillation for Efficient Pose Estimationcited by 55
2022Online Continual Learning for Embedded Devicescited by 49
2021Hessian-Aware Pruning and Optimal Neural Implantcited by 45
2021Zero-shot Adversarial Quantizationcited by 44
2021Combined Depth Space based Architecture Search For Person Re-identificationcited by 44
2021Manifold Regularized Dynamic Network Pruningcited by 43
2022Low-rank lottery tickets: finding efficient low-rank neural networks via matrix differential equationscited by 43
2021Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decompositioncited by 33
2023A Good Student is Cooperative and Reliable: CNN-Transformer Collaborative Learning for Semantic Segmentationcited by 33
2022Enable Deep Learning on Mobile Devices: Methods, Systems, and Applicationscited by 32
2021Ps and Qs: Quantization-aware pruning for efficient low latency neural network inferencecited by 31
2021Smart at what cost? Characterising Mobile Deep Neural Networks in the wildcited by 25
2023Cuttlefish: Low-Rank Model Training without All the Tuningcited by 25
2023BiBench: Benchmarking and Analyzing Network Binarizationcited by 22
2023EdgeFM: Leveraging Foundation Model for Open-set Learning on the Edgecited by 21
2021Learned Gradient Compression for Distributed Deep Learningcited by 20
2023PowerQuant: Automorphism Search for Non-Uniform Quantizationcited by 20
2022OPQ: Compressing Deep Neural Networks with One-shot Pruning-Quantizationcited by 19
2021Stealthy Backdoors as Compression Artifactscited by 18
2022QuantFace: Towards Lightweight Face Recognition by Synthetic Data Low-bit Quantizationcited by 18
2025EAC-MoE: Expert-Selection Aware Compressor for Mixture-of-Experts Large Language Modelscited by 18
2021"BNN - BN = ?": Training Binary Neural Networks without Batch Normalizationcited by 17
2024Rethinking Centered Kernel Alignment in Knowledge Distillationcited by 17
2021Fast Walsh-Hadamard Transform and Smooth-Thresholding Based Binary Layers in Deep Neural Networkscited by 14
2021Binary Complex Neural Network Acceleration on FPGAcited by 14
2023One-bit Flip is All You Need: When Bit-flip Attack Meets Model Trainingcited by 14
2023Computation-efficient Deep Learning for Computer Vision: A Surveycited by 14
2023Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challengescited by 14
2022Block Walsh-Hadamard Transform Based Binary Layers in Deep Neural Networkscited by 13
2022HuBERT-EE: Early Exiting HuBERT for Efficient Speech Recognitioncited by 13
2024Hadamard Adapter: An Extreme Parameter-Efficient Adapter Tuning Method for Pre-trained Language Modelscited by 13
2024PrefixKV: Adaptive Prefix KV Cache is What Vision Instruction-Following Models Need for Efficient Generationcited by 13
2021SeReNe: Sensitivity based Regularization of Neurons for Structured Sparsity in Neural Networkscited by 12
2021BSNN: Towards Faster and Better Conversion of Artificial Neural Networks to Spiking Neural Networks with Bistable Neuronscited by 12
2021FORMS: Fine-grained Polarized ReRAM-based In-situ Computation for Mixed-signal DNN Acceleratorcited by 12
2021APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Corescited by 12
2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challengescited by 12
2021Deep Network Approximation in Terms of Intrinsic Parameterscited by 12

50 most cited of 215 All 215 (JSON)