Awesome-Model-Quantization
A list of papers, docs, codes about model quantization. This repo is aimed to provide the info for model quantization research, we are continuously improving the project. Welcome to PR the works (papers, repositories) that are missed by the repo.
https://github.com/AI-Efficiency/Awesome-Model-Quantization
Last synced: about 4 hours ago
JSON representation
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Benchmarks
- [Paper
- [Paper - Quantization)] [](https://github.com/Macaronlin/LLaMA3-Quantization)
- [Paper - ML/Qwen3-Quantization)] [](https://github.com/Efficient-ML/Qwen3-Quantization)
- [Paper
- [Paper
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Books
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Papers
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2015
-
2016
- [CVPR - wu/quantized-cnn)
- [arXiv - Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients [[code](https://github.com/tensorpack/tensorpack/tree/master/examples/DoReFa-Net)] [](https://github.com/tensorpack/tensorpack)
- [ECCV - Net: ImageNet Classification Using Binary Convolutional Neural Networks [[code](https://github.com/allenai/XNOR-Net)] [](https://github.com/allenai/XNOR-Net)
- [ICASSP - point Performance Analysis of Recurrent Neural Networks
- [NeurIPS - 1 [[code](https://github.com/itayhubara/BinaryNet)] [](https://github.com/itayhubara/BinaryNet)
- [NeurIPS - chris/caffe-twns)] [](https://github.com/fengfu-chris/caffe-twns)
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2017
- [CVPR - wave Gaussian Quantization [[code](https://github.com/zhaoweicai/hwgq)] [](https://github.com/zhaoweicai/hwgq)
- [CVPR
- [arXiv - Source Binary Neural Network Implementation Based on MXNet [[code](https://github.com/hpi-xnor)]
- [FPGA
- [ICASSP - point optimization of deep neural networks with adaptive step size retraining
- [ICCV - cnn-landmarks)]
- [ICCV - Order Residual Quantization
- [ICLR - Precision Weights [[code](https://github.com/Mxbonn/INQ-pytorch)] [](https://github.com/Mxbonn/INQ-pytorch)
- [ICLR - aware Binarization of Deep Networks [[code](https://github.com/houlu369/Loss-aware-Binarization)] [](https://github.com/houlu369/Loss-aware-Binarization)
- [ICLR - Sharing for Neural Network Compression
- [ICLR - ternary-quantization)] [](https://github.com/TropComplique/trained-ternary-quantization)
- [IPDPSW - Chip Memory Based Binarized Convolutional Deep Neural Network Applying Batch Normalization Free Technique on an FPGA
- [InterSpeech
- [JETC - Outperforming FPGA Accelerator Architecture for Binary Convolutional Neural Networks
- [MWSCAS
- [NeurIPS - Binary-Convolution-Network)] [](https://github.com/layog/Accurate-Binary-Convolution-Network)
- [Neurocomputing - BNN: Binarized neural network on FPGA
- [arXiv - Precision Architecture for Inference of Convolutional Neural Networks [[code](https://github.com/gudovskiy/ShiftCNN)] [](https://github.com/gudovskiy/ShiftCNN)
- [arXiv - Grained Quantization
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2018
- [AAAI
- [AAAI
- [CAAI
- [CVPR - bitwidth Weights and Activations
- [CVPR - error-aware quantization for low-bit deep neural networks
- [CVPR
- [CVPR - Arithmetic-Only Inference
- [CVPR
- [CVPR - bitwidth Convolutional Neural Networks
- [CVPR - Step Quantization for Low-bit Neural Networks
- [arXiv
- [arXiv
- [ECCV - Real Net: Enhancing the Performance of 1-bit CNNs With Improved Representational Capability and Advanced Training Algorithm [[code](https://github.com/liuzechun/Bi-Real-net)] [](https://github.com/liuzechun/Bi-Real-net)
- [ECCV - Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks [[code](https://github.com/microsoft/LQ-Nets)] [](https://github.com/microsoft/LQ-Nets)
- [ECCV
- [ECCV
- [ECCV - Binary Decomposition
- [FCCM
- [FPL
- [ICLR
- [ICLR - Precision Network Accuracy
- [ICLR - aware Weight Quantization of Deep Networks [[code](https://github.com/houlu369/Loss-aware-weight-quantization)] [](https://github.com/houlu369/Loss-aware-weight-quantization)
- [ICLR
- [ICLR - Precision Networks
- [IEEE J. Solid-State Circuits - Chip Binary/Ternary Reconfigurable in-Memory Deep Neural Network Accelerator Achieving 1.4 TOPS at 0.6 W
- [IJCAI
- [IJCAI
- [IJCNN
- [IPDPS
- [MM - Time Low-Power Inference of Binary Neural Networks on CPUs
- [NCA - based accelerators for convolutional neural networks
- [NeurIPS - bit training of neural networks [[code](https://github.com/eladhoffer/quantized.pytorch)] [](https://github.com/eladhoffer/quantized.pytorch)
- [NeurIPS - bit Floating Point Numbers
- [Res Math Sci
- [TCAD - fJ/op Binary Neural Network Inference
- [TRETS - R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks
- [TVLSI - Efficient Architecture for Binary Weight Convolutional Neural Networks
- [arXiv - quantization)] [](https://github.com/yukang2017/NAS-quantization)
- [arXiv - xnor/BMXNet-v2)] [](https://github.com/hpi-xnor/BMXNet-v2)
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2019
- [AAAI
- [AAAI - bit CNNs via Discrete Back Propagation
- [APCCAS
- [BMVC
- [BMVC - Net++: Improved Binary Neural Networks
- [CVPR
- [CVPR
- [CVPR - bit DCNNs with Circulant Back Propagation
- [CVPR
- [CVPR - Aware Automated Quantization with Mixed Precision [[code](https://github.com/mit-han-lab/haq)] [](https://github.com/mit-han-lab/haq)
- [CVPR - Wise Interactions for Binary Convolutional Neural Networks
- [CVPR
- [CVPR - quantization-networks)] [](https://github.com/aliyun/alibabacloud-quantization-networks)
- [CVPR
- [CVPR - Map Sparsity Through Low-Bit Quantization
- [CVPR
- [arXiv - xnor/BMXNet-v2)] [](https://github.com/hpi-xnor/BMXNet-v2)
- [arXiv
- [arXiv
- [arXiv
- [arXiv - bit DCNNs
- [arXiv - Ensemble Template for Accurate Binary Convolutional Neural Networks
- [FPGA - Efficient Binarized Neural Network Inference on FPGA
- [GLSVLSI
- [ICCV - bit cnns
- [ICCV - Free Quantization Through Weight Equalization and Bias Correction [[code](https://github.com/jakc4103/DFQ)] [](https://github.com/jakc4103/DFQ)
- [ICCV - Precision and Low-Bit Neural Networks
- [ICCV
- [ICCV - Precision
- [ICCV
- [ICIP - xnor/BMXNet-v2)] [](https://github.com/hpi-xnor/BMXNet-v2)
- [ICLR
- [ICLR
- [ICML - Bit Quantization of Transformer Neural Machine Language Translation Model
- [ICUS
- [IEEE J. Emerg. Sel. Topics Circuits Syst. - Chip Systolically Scalable Binary-Weight CNN Inference Engine
- [IEEE J. Solid-State Circuits - Efficient Reconfigurable Processor for Binary-and Ternary-Weight Neural Networks With Flexible Data Bit Width
- [IEEE JETC
- [IEEE TCS.I - Chip Memory
- [IEEE TCS.I - RAM: Accelerating Binary Neural Networks in High-Throughput SRAM Compute Arrays
- [IJCAI
- [IJCAI - Efficient Hashing with Minimizing Quantization Loss
- [ISOCC
- [MDPI Electronics
- [NeurIPS
- [NeurIPS - bnn-optimization)] [](https://github.com/plumerai/rethinking-bnn-optimization)
- [NeurIPS - differentiable Quantization [[code](https://github.com/csyhhu/MetaQuant)] [](https://github.com/csyhhu/MetaQuant)
- [NeurIPS
- [NeurIPS
- [NeurIPS
- [NeurIPS
- [RoEduNet
- [SiPS
- [TMM
- [TMM - Modal Hashing
- [VLSI-SoC - Efficient Execution of Binary Neural Networks Using Resistive Memories
- [arXiv - CV/dabnn)] [](https://github.com/JDAI-CV/dabnn)
- [arXiv
- [arXiv - aware Knowledge Distillation
- [arXiv - Binarizing Networks
- [arXiv
- [paper
-
2020
- [CVPR - Net)] [](https://github.com/htqin/IR-Net)
- [PR
- [AAAI - BERT: Hessian Based Ultra Low Precision Quantization of BERT
- [ACL
- [COOL CHIPS - DRAM Accelerator Architecture for Binary Neural Network
- [CVPR - han-lab/apq)] [](https://github.com/mit-han-lab/apq)
- [CVPR
- [CVPR - Point Back-Propagation Training
- [CVPR
- [CVPR - Bit Quantization Needs Good Distribution
- [CVPR - Bit Face Recognition
- [arXiv
- [DATE - based computing systems
- [DATE
- [DATE - Accelerated Binary Neural Network Inference Engine for Mobile Phones
- [ECCV
- [ECCV
- [ECCV - bitwidth Data Free Quantization [[code](https://github.com/xushoukai/GDFQ)] [](https://github.com/xushoukai/GDFQ)
- [ECCV
- [ECCV - 4-bit MobileNet Models
- [ECCV
- [ECCV
- [EMNLP
- [EMNLP - aware Ultra-low Bit BERT [[code](https://github.com/huawei-noah/Pretrained-Language-Model)] [](https://github.com/huawei-noah/Pretrained-Language-Model)
- [ICASSP
- [ICET - Efficient Bagged Binary Neural Network Accelerator
- [ICLR - K1m/BinaryDuo)] [](https://github.com/Hyungjun-K1m/BinaryDuo)
- [ICLR
- [ICLR
- [ICLR - research-code/tree/master/mixed-precision-dnns)] [](https://github.com/sony/ai-research-code)
- [ICLR - to-Binary Convolutions
- [ICML - Scale Inference with Anisotropic Vector Quantization
- [ICML - bit quantization through learnable offsets and better initialization
- [ICML
- [ICML - Training Quantization
- [IEEE Access - Efficient and High Throughput in-Memory Computing Bit-Cell With Excellent Robustness Under Process Variations for Binary Neural Network
- [IEEE TCS.I - Memory Multi-Bit Multiplication and ACcumulation in 6T SRAM Array
- [IEEE TCS.II - Efficient Inference Accelerator for Binary Convolutional Neural Networks
- [IEEE Trans. Electron Devices
- [IEEE Trans. Magn - Memory Binary Neural Network Accelerator
- [IJCAI - NAS: Child-Parent Neural Architecture Search for Binary Neural Networks
- [IJCAI - width Deep Neural Networks
- [IJCAI
- [IJCAI - bit Multiply-Accumulate Operations
- [IJCAI
- [IJCAI - bit Integer Inference for the Transformer Model
- [IJCV
- [ISCAS - Level Binarized Recurrent Neural Network for EEG Signal Classification
- [ISQED - ASU/BNNPruning)] [](https://github.com/PSCLab-ASU/BNNPruning)
- [MICRO - Based NLP Models for Low Latency and Energy Efficient Inference
- [MLST
- [NN - performance and large-scale deep neural networks with full 8-bit integers
- [NeurIPS - Parallel SGD [[code](https://github.com/tabrizian/learning-to-quantize)] [](https://github.com/tabrizian/learning-to-quantize)
- [NeurIPS
- [NeurIPS - Layer Flow [[code](https://github.com/didriknielsen/pixelcnn_flow)] [](https://github.com/didriknielsen/pixelcnn_flow)
- [NeurIPS - kai/eevbnn)] [](https://github.com/jia-kai/eevbnn)
- [NeurIPS
- [NeurIPS - V2: Hessian Aware trace-Weighted Quantization of Neural Networks
- [NeurIPS - Analytic Gradient Estimators for Stochastic Binary Networks [[code](https://github.com/shekhovt/PSA-Neurips2020)] [](https://github.com/shekhovt/PSA-Neurips2020)
- [NeurIPS - based Scaled Gradient for Model Quantization and Pruning [[code](https://github.com/Jangho-Kim/PSG-pytorch)] [](https://github.com/Jangho-Kim/PSG-pytorch)
- [NeurIPS
- [NeurIPS
- [NeurIPS - Bit Weights in Quantized Neural Networks [[code](https://github.com/zhaohui-yang/Binary-Neural-Networks/tree/main/SLB)] [](https://github.com/zhaohui-yang/Binary-Neural-Networks)
- [NeurIPS
- [Neurocomputing
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