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/Efficient-ML/Awesome-Model-Quantization
Last synced: 3 days ago
JSON representation
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Papers
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2020
- [ICML - Scale Inference with Anisotropic Vector Quantization.
- [CVPR - han-lab/apq)] [76:star:]
- [ECCV - -citation 6-->
- [CVPR - Net)] [105:star:] <!--citation 15-->
- [ACL - -citation 0-->
- [AAAI
- [AAAI - BERT: Hessian Based Ultra Low Precision Quantization of BERT. [__`qnn`__]
- [AAAI - Inducing Binarized Neural Networks. [**`bnn`**]
- [AAAI - Width Quantization with Multiple Phase Adaptations.
- [COOL CHIPS - DRAM Accelerator Architecture for Binary Neural Network. [**`hardware`**] <!--citation 0-->
- [CoRR
- [CVPR - noah/ghostnet)] [1.2k:star:] <!--citation 47-->
- [CVPR - Bit Face Recognition. [__`qnn`__]
- [CVPR - -citation 3-->
- [CVPR - Point Back-Propagation Training. [[video](https://www.youtube.com/watch?v=nVRNygIQKI0)] [__`qnn`__]
- [CVPR - Bit Quantization Needs Good Distribution. [**`qnn`**] <!--citation 1-->
- [ICML - bit quantization through learnable offsets and better initialization
- [DATE - based computing systems. [**`bnn`**] <!--citation 1-->
- [DATE - Accelerated Binary Neural Network Inference Engine for Mobile Phones. [**`bnn`**] [**`hardware`**]
- [DATE - -citation 2-->
- [ECCV - -citation 5-->
- [ECCV - 4-bit MobileNet Models. [**`qnn`**] <!--citation 2-->
- [ECCV - -citation 2-->
- [ECCV - -citation 7-->
- [ECCV - bitwidth Data Free Quantization. [**`qnn`**] [[torch](https://github.com/xushoukai/GDFQ)]
- [EMNLP - aware Ultra-low Bit BERT. [**`qnn`**]
- [EMNLP
- [ICET - Efficient Bagged Binary Neural Network Accelerator. [**`bnn`**] [**`hardware`**] <!--citation 0-->
- [ICASSP - -citation 3-->
- [ICML - -citation 5-->
- [ICLR
- [ICLR - to-Binary Convolutions. [**`bnn`**] [[code is comming](https://github.com/brais-martinez/real2binary)] [[re-implement](https://github.com/larq/zoo/blob/master/larq_zoo/literature/real_to_bin_nets.py)] <!--citation 19-->
- [ICLR - K1m/BinaryDuo)] <!--citation 6-->
- [ICLR - research-code/tree/master/mixed-precision-dnns)] [73:star:]
- [ICLR
- [IJCV - -citation 0-->
- [IJCAI - NAS: Child-Parent Neural Architecture Search for Binary Neural Networks. [**`bnn`**]
- [IJCAI - bit Integer Inference for the Transformer Model. [**`qnn`**] [**`nlp`**]
- [IJCAI
- [IJCAI - bit Multiply-Accumulate Operations. [**`qnn`**]
- [IJCAI - width Deep Neural Networks. [**`qnn`**]
- [IJCAI
- [ISCAS - Level Binarized Recurrent Neural Network for EEG Signal Classification. [**`bnn`**] <!--citation 0-->
- [ISQED - ASU/BNNPruning)] <!--citation 0-->
- [MICRO - Based NLP Models for Low Latency and Energy Efficient Inference. [**`qnn`**] [**`nlp`**]
- [MLST - -citation 11-->
- [NeurIPS
- [NeurIPS - Bit Weights in Quantized Neural Networks. [**`qnn`**] [[torch](https://github.com/zhaohui-yang/Binary-Neural-Networks/tree/main/SLB)] <!--citation 4-->
- [NeurIPS
- [NeurIPS - kai/eevbnn)]
- [NeurIPS - Analytic Gradient Estimators for Stochastic Binary Networks. [**`bnn`**] [[code](https://github.com/shekhovt/PSA-Neurips2020)]
- [NeurIPS - V2: Hessian Aware trace-Weighted Quantization of Neural Networks. [**`qnn`**]
- [NeurIPS
- [NeurIPS
- [NeurIPS - Layer Flow. [**`qnn`**] [[torch](https://github.com/didriknielsen/pixelcnn_flow)]
- [NeurIPS - Parallel SGD. [**`qnn`**] [[torch](https://github.com/tabrizian/learning-to-quantize)]
- [NeurIPS
- [NeurIPS - based Scaled Gradient for Model Quantization and Pruning. [**`qnn`**] [[torch](https://github.com/Jangho-Kim/PSG-pytorch)]
- [NN - performance and large-scale deep neural networks with full 8-bit integers. [**`qnn`**] <!--citation 13-->
- [Neurocomputing
- [PR Letters - -citation 0-->
- [SysML - to-End Binarized Neural Networks. [**`qnn`**] [[tensorflow](https://github.com/jwfromm/Riptide)] [129:star:] <!--citation 5-->
- [TPAMI - cnn-landmarks)] [[code](https://github.com/1adrianb/binary-human-pose-estimation)]
- [TPAMI - Nets: A Coupled and Quantized Approach.
- [TVLSI - Precision Floating-Point Quantization Oriented Architecture for Convolutional Neural Networks. [**`qnn`**] <!--citation 0-->
- [WACV - -citation 11-->
- [IEEE Access - Efficient and High Throughput in-Memory Computing Bit-Cell With Excellent Robustness Under Process Variations for Binary Neural Network. [**`bnn`**] [**`hardware`**] <!--citation 0-->
- [IEEE Trans. Magn - Memory Binary Neural Network Accelerator. [**`bnn`**] <!--citation 0-->
- [IEEE TCS.II - Efficient Inference Accelerator for Binary Convolutional Neural Networks. [**`hardware`**] <!--citation 1-->
- [IEEE TCS.I - Memory Multi-Bit Multiplication and ACcumulation in 6T SRAM Array. [**`qnn`**] <!--citation 3-->
- [IEEE Trans. Electron Devices - -citation 0-->
- [arXiv
- [arXiv - -citation 0-->
- [arXiv - -citation 5-->
- [arXiv - -citation 1-->
- [arXiv - Tensor-Cores in Turing GPUs. [**`bnn`**] [[code](https://github.com/pnnl/TCBNN)] <!--citation 1-->
- [arXiv - level Accuracy? [**`bnn`**] [[code](https://github.com/hpi-xnor/BMXNet-v2)] [192:star:] <!--citation 13-->
- [arXiv - -citation 3-->
- [paper - -citation 2-->
- [arXiv - -citation 0-->
- [arXiv
- [ECCV
- [COOL CHIPS - DRAM Accelerator Architecture for Binary Neural Network. [**`hardware`**] <!--citation 0-->
- [IJCAI - NAS: Child-Parent Neural Architecture Search for Binary Neural Networks. [**`bnn`**]
- [ISCAS - Level Binarized Recurrent Neural Network for EEG Signal Classification. [**`bnn`**] <!--citation 0-->
- [TPAMI - cnn-landmarks)] [[code](https://github.com/1adrianb/binary-human-pose-estimation)]
- [TPAMI - Parallel Pruning-Quantization.
- [TPAMI - Nets: A Coupled and Quantized Approach.
- [TVLSI - Precision Floating-Point Quantization Oriented Architecture for Convolutional Neural Networks. [**`qnn`**] <!--citation 0-->
- [IEEE Access - Efficient and High Throughput in-Memory Computing Bit-Cell With Excellent Robustness Under Process Variations for Binary Neural Network. [**`bnn`**] [**`hardware`**] <!--citation 0-->
- [IEEE TCS.II - Efficient Inference Accelerator for Binary Convolutional Neural Networks. [**`hardware`**] <!--citation 1-->
- [IEEE TCS.I - Memory Multi-Bit Multiplication and ACcumulation in 6T SRAM Array. [**`qnn`**] <!--citation 3-->
- [arXiv - -citation 5-->
- [arXiv - -citation 1-->
- [arXiv - -citation 3-->
- [ECCV
- [arXiv - -citation 0-->
- [MICRO - Based NLP Models for Low Latency and Energy Efficient Inference. [**`qnn`**] [**`nlp`**]
- [ACL - -citation 0-->
- [ICML - Training Quantization
- [CVPR
- [CVPR - Widths [[code](https://github.com/deJQK/AdaBits)] [](https://github.com/deJQK/AdaBits)
- [CVPR - aware Quantization for Multi-bit Networks [[code](https://github.com/zqu1992/ALQ)] [](https://github.com/zqu1992/ALQ)
- [ECCV - si/ai-research)] [](https://github.com/sony-si/ai-research)
- [AAAI
- [AAAI - Inducing Binarized Neural Networks
- [AAAI - Width Quantization with Multiple Phase Adaptations
-
2016
- [ICASSP - point Performance Analysis of Recurrent Neural Networks. [**`qnn`**]
- [ECCV - Net: ImageNet Classification Using Binary Convolutional Neural Networks. [**`bnn`**] [[torch](https://github.com/allenai/XNOR-Net)] [787:star:]
- [CoRR - Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients. [**`qnn`**] [[code](https://github.com/tensorpack/tensorpack/tree/master/examples/DoReFa-Net)] [5.8k:star:]
- [NeurIPS - chris/caffe-twns)] [61:star:]
- [CVPR - wu/quantized-cnn)
- [NeurIPS - 1. [**`bnn`**] [[torch](https://github.com/itayhubara/BinaryNet)] [239:star:]
-
2017
- [arXiv - Precision Architecture for Inference of Convolutional Neural Networks. [**`qnn`**] [[code](https://github.com/gudovskiy/ShiftCNN)] [53:star:]
- [CoRR - Source Binary Neural Network Implementation Based on MXNet. [**`bnn`**] [[code](https://github.com/hpi-xnor)]
- [CVPR - wave Gaussian Quantization. [**`qnn`**] [[code](https://github.com/zhaoweicai/hwgq)] [118:star:]
- [CVPR
- [FPGA
- [ICASSP - point optimization of deep neural networks with adaptive step size retraining. [**`qnn`**]
- [ICCV - cnn-landmarks)] [[torch](https://github.com/1adrianb/binary-human-pose-estimation)] [207:star:]
- [ICCV - Order Residual Quantization. [**`qnn`**]
- [ICLR - Precision Weights. [**`qnn`**] [[torch](https://github.com/Mxbonn/INQ-pytorch)] [144:star:]
- [ICLR - aware Binarization of Deep Networks. [**`bnn`**] [[code](https://github.com/houlu369/Loss-aware-Binarization)]
- [ICLR - Sharing for Neural Network Compression. [__`other`__]
- [ICLR - ternary-quantization)] [90:star:]
- [InterSpeech
- [IPDPSW - Chip Memory Based Binarized Convolutional Deep Neural Network Applying Batch Normalization Free Technique on an FPGA. [**`hardware`**]
- [JETC - Outperforming FPGA Accelerator Architecture for Binary Convolutional Neural Networks. [**`hardware`**] [**`bnn`**]
- [NeurIPS - Binary-Convolution-Network)]
- [Neurocomputing - BNN: Binarized neural network on FPGA. [**`hardware`**]
- [arXiv - Grained Quantization. [**`qnn`**]
- [MWSCAS
- [Neurocomputing - BNN: Binarized neural network on FPGA. [**`hardware`**]
- [MWSCAS
-
2018
- [ICLR - Precision Network Accuracy. [**`qnn`**]
- [AAAI
- [AAAI
- [CAAI
- [CoRR
- [CoRR
- [CVPR - Step Quantization for Low-bit Neural Networks. [**`qnn`**]
- [CVPR - bitwidth Weights and Activations. [**`qnn`**]
- [CVPR - bitwidth Convolutional Neural Networks. [**`qnn`**]
- [CVPR
- [CVPR
- [CVPR - Arithmetic-Only Inference. [**`qnn`**]
- [ECCV - Binary Decomposition. [**`bnn`**]
- [ECCV
- [ECCV - Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks. [**`qnn`**] [[tensorflow](https://github.com/microsoft/LQ-Nets)] [188:star:]
- [ECCV - Real Net: Enhancing the Performance of 1-bit CNNs With Improved Representational Capability and Advanced Training Algorithm. [**`bnn`**] [[torch](https://github.com/liuzechun/Bi-Real-net)] [120:star:]
- [ECCV
- [FCCM
- [FPL
- [ICLR - aware Weight Quantization of Deep Networks. [**`qnn`**] [[code](https://github.com/houlu369/Loss-aware-weight-quantization)]
- [ICLR
- [ICLR
- [ICLR - Precision Networks. [**`qnn`**]
- [ICLR
- [IJCAI
- [IJCAI - -citation 14-->
- [IJCNN
- [IPDPS
- [NCA - based accelerators for convolutional neural networks. [**`hardware`**]
- [NeurIPS - bit Floating Point Numbers. [**`qnn`**]
- [NeurIPS - bit training of neural networks. [**`qnn`**] [[torch](https://github.com/eladhoffer/quantized.pytorch)]
- [Res Math Sci
- [TCAD - fJ/op Binary Neural Network Inference. [**`hardware`**]
- [TRETS - R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks. [**`qnn`**]
- [arXiv - xnor/BMXNet-v2)] [192:star:]
- [arXiv - quantization)]
- [CVPR - error-aware quantization for low-bit deep neural networks. [**`qnn`**]
- [IEEE J. Solid-State Circuits - Chip Binary/Ternary Reconfigurable in-Memory Deep Neural Network Accelerator Achieving 1.4 TOPS at 0.6 W. [**`hardware`**] [**`qnn`**]
- [TVLSI - Efficient Architecture for Binary Weight Convolutional Neural Networks. [**`bnn`**]
- [CoRR
- [FPL
- [IEEE J. Solid-State Circuits - Chip Binary/Ternary Reconfigurable in-Memory Deep Neural Network Accelerator Achieving 1.4 TOPS at 0.6 W. [**`hardware`**] [**`qnn`**]
- [NCA - based accelerators for convolutional neural networks. [**`hardware`**]
- [MM - Time Low-Power Inference of Binary Neural Networks on CPUs. [**`bnn`**]
- [TCAD - fJ/op Binary Neural Network Inference. [**`hardware`**]
- [TRETS - R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks. [**`qnn`**]
- [TVLSI - Efficient Architecture for Binary Weight Convolutional Neural Networks. [**`bnn`**]
- [FCCM
- [ICLR
-
2021
- [arXiv - Training Quantization for Vision Transformer. [**`qnn`**]
- [ICLR - shot learning via vector quantization in deep embedded space. [__`qnn`__]
- [CVPR - tune: Efficient Compression of Neural Networks. [__`qnn`__] [[torch](https://github.com/uber-research/permute-quantize-finetune)] [137⭐]
- [ICLR - Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Network. [**`bnn`**]
- [ICLR
- [ICML
- [ICML - Bit Activation Compressed Training [**`qnn`**]
- [ICML - V3: Dyadic Neural Network Quantization. [**`qnn`**]
- [ICML - BERT: Integer-only BERT Quantization. [**`qnn`**]
- [ICML
- [ICML - NBA: Efficient and Effective Search Over the Joint Space of Networks, Bitwidths, and Accelerators. [**`qnn`**]
- [ICCV - Free Compression Are Feature and Data Mixing.
- [CVPR - bnn: Bridging the gap between self-supervised real and 1-bit neural networks via guided distribution calibration [**`bnn`**] [[code](https://github.com/szq0214/S2-BNN)] [52⭐]
- [CVPR - Free Quantization. [__`qnn`__]
- [ACM MM - Resolution Networks. [**`qnn`**]
- [NeurIPS - free Quantization with Synthetic Boundary Supporting Samples. [__`qnn`__]
- [NeurIPS - Training Quantization for Vision Transformer. [__`mixed`__]
- [NeurIPS - Training Sparsity-Aware Quantization. [__`qnn`__]
- [NeurIPS
- [NeurIPS - GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization. [__`other`__]
- [NeurIPS - ANTI-zation: Exploiting Quantization Artifacts for Achieving Adversarial Outcomes .
- [NeurIPS - of-Distribution Robustness. [**`bnn`**] [[torch](https://github.com/chrundle/biprop)]
- [CVPR - bit Neural Networks. [__`qnn`__]
- [CVPR - shot Adversarial Quantization. [__`qnn`__] [[torch](https://github.com/FLHonker/ZAQ-code)]
- [CVPR
- [CVPR - wise Gradient Scaling. [__`qnn`__] [[torch](https://github.com/cvlab-yonsei/EWGS)]
- [CVPR
- [ICLR
- [ICLR - Capacity Expert Binary Networks. [**`bnn`**]
- [ICLR - Training Quantization by Block Reconstruction. [__`qnn`__] [[torch](https://github.com/yhhhli/BRECQ)]
- [ICLR - lognormal: improved quantized and sparse training. [__`qnn`__]
- [ICLR
- [ICLR - Quant: Quantization-Aware Training for Graph Neural Networks. [__`qnn`__]
- [ICLR - Level Sparsity for Mixed-Precision Neural Network Quantization. [__`qnn`__]
- [ICLR
- [ICLR
- [ICLR - Low-Resolution Arithmetic. [__`qnn`__]
- [ECCV - Resolution via Parameterized Max Scale. [__`qnn`__]
- [AAAI
- [AAAI
- [AAAI - Precision Activation Quantization. [__`qnn`__]
- [AAAI - shot Pruning-Quantization. [__`qnn`__]
- [AAAI
- [AAAI
- [AAAI - Widths. [__`qnn`__]
- [AAAI - ‐training Quantization with Multiple Points: Mixed Precision without Mixed Precision. [__`qnn`__]
- [AAAI
- [AAAI
- [AAAI - Dimensional Binary Convolution Filters. [**`bnn`**]
- [ACL - time Quantization of Attention Values in Transformers. [__`qnn`__]
- [arXiv - Precision Deep Neural Networks. [__`mixed`__] [[torch](https://github.com/SHI-Labs/Any-Precision-DNNs)]
- [arXiv - hXu/ReCU)]
- [arXiv
- [arXiv
- [ACM MM - hops Graph Reasoning for Explicit Representation Learning. [__`other`__]
- [AAAI
- [AAAI - ‐training Quantization with Multiple Points: Mixed Precision without Mixed Precision. [__`qnn`__]
- [AAAI
- [AAAI - Efficient Kernel SVM via Binary Embedding and Ternary Coefficients. [**`bnn`**]
- [AAAI - Dimensional Binary Convolution Filters. [**`bnn`**]
- [ACL - time Quantization of Attention Values in Transformers. [__`qnn`__]
- [ICML
- [ICLR - Low-Resolution Arithmetic. [__`qnn`__]
- [AAAI - Precision Activation Quantization. [__`qnn`__]
- [AAAI
- [arXiv - hXu/ReCU)]
- [arXiv
- [AAAI - Resolution
- [AAAI - BNN: State-Aware Binary Neural Network
- [CVPR - Scale Quantization for Low-Cost Deep Neural Networks
- [CVPR - Time Quantization Parameter Prediction for Deep Neural Networks
- [ICLR - wise Calibration and Integer Programming [[code](https://github.com/itayhubara/CalibTIP)] [](https://github.com/itayhubara/CalibTIP)
- [ICML
- [NeurIPS - for-all Architecture Search with Robust Quantizer
- [AAAI
- [AAAI - shot Pruning-Quantization
- [CVPR - bnn: Bridging the gap between self-supervised real and 1-bit neural networks via guided distribution calibration [[code](https://github.com/szq0214/S2-BNN)] [](https://github.com/szq0214/S2-BNN)
-
2022
- [IJCAI - ViT: Post-Training Quantization for Fully Quantized Vision Transformer. [__`qnn`__] [[code](https://github.com/megvii-research/FQ-ViT)] [71:star:]
- [ACM MM - Training Quantizationfor Vision Transformer
- [NeurIPS - bit Matrix Multiplication for Transformers at Scale
- [NeurIPS - bit Transformer Language Models. [[code](https://github.com/wimh966/outlier_suppression)]
- [arXiv - Training Quantization for Large Language Models [__`qnn`__] [[code](https://github.com/mit-han-lab/smoothquant)] [150:star:]
- [ECCV
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [IJCV - sensitive Information Retention for Accurate Binary Neural Network. [__`bnn`__]
- [ICML
- [ICML - Optimal Low-Bit Sub-Distribution in Deep Neural Networks [**`qnn`**] [**`hardware`**]
- [ICML
- [ICLR
- [CVPR - SNN: Rectifying Membrane Potential Distribution for Directly Training Spiking Neural Networks. [**`snn`**]
- [CVPR - Shot Quantization Brought Closer to the Teacher. [**`qnn`**] [[code](https://github.com/iamkanghyunchoi/ait)]
- [CVPR - to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation. [__`qnn`__]
- [CVPR
- [CVPR - Training Non-Uniform Quantization based on Minimizing the Reconstruction Error. [__`qnn`__]
- [CVPR - Free Network Compression via Parametric Non-uniform Mixed Precision Quantization. [__`qnn`__]
- [CVPR - Aware Dynamic Neural Network Quantization. [__`qnn`__]
- [NeurIPS - distilled Transformer. [**`bnn`**] [[code](https://github.com/facebookresearch/bit)] [42⭐]
- [NeurIPS - Layer Dependency for Post -Training Quantization. [__`qnn`__]
- [NeurIPS - Aware Quantization Techniques. [__`qnn`__]
- [NeurIPS - Driven Mixed-Precision Quantization for Deep Network Design. [__`qnn`__]
- [NeurIPS
- [NeurIPS
- [NeurIPS - training Quantization of Pre-trained Language Models. [__`qnn`__]
- [NeurIPS - Training Quantization and Pruning. [__`qnn`__] [**`hardware`**]
- [NeurIPS - Training Quantization for Large-Scale Transformers. [__`qnn`__]
- [NeurIPS
- [NeurIPS - ViT: Accurate and Fully Quantized Low-bit Vision Transformer. [__`qnn`__]
- [NeurIPS - Layer Perceptrons. [**`bnn`**] [[code](https://gitee.com/mindspore/models/tree/master/research/cv/BiMLP)]
- [ECCV - Uniform Step Size Quantization for Accurate Post-Training Quantization. [__`qnn`__]
- [ECCV - Training Quantization for Vision Transformers with Twin Uniform Quantization. [__`qnn`__]
- [ECCV
- [ECCV - wise Activation-clipping Search Quantization for Sub-4-bit Neural Networks. [__`qnn`__]
- [ECCV - Q: Extremely Fine-Grained Channel-Wise Quantization via Rate-Distortion Optimization. [__`qnn`__]
- [ECCV - Precision Neural Network Quantization via Learned Layer-Wise Importance. [__`qnn`__] [[Code](https://github.com/1hunters/LIMPQ)]
- [ECCV
- [ECCV - Free Quantization for Vision Transformers. [__`qnn`__]
- [IJCAI
- [IJCAI - of-Two Low-bit Post-training Quantization. [__`qnn`__]
- [IJCAI - bit Quantization of Neural Networks. [__`qnn`__]
- [ICLR - Point 8-bit Only Multiplication for Network Quantization. [**`qnn`**]
- [ICLR - bit Optimizers via Block-wise Quantization. [**`qnn`**]
- [ICLR - Precision Training: Data Format Optimization and Hysteresis Quantization. [**`qnn`**]
- [ICLR
- [ICLR - SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks. [**`snn`**]
- [ICLR - the-Fly Data-Free Quantization via Diagonal Hessian Approximation. [**`qnn`**][code](https://github.com/clevercool/SQuant)]
- [ICLR
- [arXiv - ViT: Fully Differentiable Quantization for Vision Transformer [__`qnn`__]
- [arXiv - training Quantization of Convolutional Neural Networks using Extreme Gradient Boosting for Fast Deployment [__`qnn`__]
- [TGARS - Based Hyperspectral Image Classification by Step Activation Quantization [__`qnn`__]
- [arxiv
- [IJNS
- [ACM Trans. Des. Autom. Electron. Syst.
- [MICRO - bit Deep Neural Network Quantization.
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [CVPR - System-Software-and-Security/BppAttack)]
- [IEEE Internet of Things Journal - Efficient Federated Learning Framework for IoT With Low-Bitwidth Neural Network Quantization.
- [Neural Networks - aware training for low precision photonic neural networks.
- [ICCRD
- [Electronics
- [Applied Soft Computing - distillation and parameter quantization for the bearing fault diagnosis.
- [CVPR - Class Heterogeneity for Zero-Shot Network Quantization. [[torch](https://github.com/zysxmu/IntraQ)]
- [Neurocomputing
- [tinyML Research Symposium - of-Two Quantization for Low Bitwidth and Hardware Compliant Neural Networks.
- [arXiv - 8-Bit Quantization Aware Training for 8-Bit Neural Network Accelerator with On-Device Speech Recognition.
- [Ocean Engineering
- [CVPR - of-Flight Depth Maps.
- [TCSVT - Q Quantization on FPGA.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [arXiv - Precision Quantized Neural Networks.
- [arXiv
- [ITSM - Powered Parking Surveillance With Quantized Neural Networks.
- [Intelligent Automation & Soft Computing - Efficient Convolutional Neural Network Accelerator Using Fine-Grained Logarithmic Quantization.
- [ICML - Aware Training. [[torch](https://github.com/qualcomm-ai-research/oscillations-qat)]
- [CCF Transactions on High Performance Computing
- [CVPR
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [TCCN - Bitwidth Convolutional Neural Networks for Wireless Interference Identification.
- [ICPR - Wise Data-Free CNN Compression.
- [IJCNN - Based Quantized Neural Networks.
- [ACL - trained Language Models via Quantization
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [TODAES - in-Memory Neural Networks Acceleration.
- [FPGA - QNN: Efficient FPGA Acceleration of Deep Neural Networks with Intra-Layer, Mixed-Precision Quantization.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [PPoPP
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [arXiv
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ASE - based Formal Verification Approach for Quantized Neural Networks.
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [arXiv - Training Quantization for Large Language Models [__`qnn`__] [[code](https://github.com/mit-han-lab/smoothquant)] [150:star:]
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [arXiv - ViT: Fully Differentiable Quantization for Vision Transformer [__`qnn`__]
- [arXiv - training Quantization of Convolutional Neural Networks using Extreme Gradient Boosting for Fast Deployment [__`qnn`__]
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [tinyML Research Symposium - of-Two Quantization for Low Bitwidth and Hardware Compliant Neural Networks.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [CVPR - System-Software-and-Security/BppAttack)]
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [Neurocomputing
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [IJNS
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ACL - trained Language Models via Quantization
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [Neural Networks - aware training for low precision photonic neural networks.
- [Applied Soft Computing - distillation and parameter quantization for the bearing fault diagnosis.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [arXiv - 8-Bit Quantization Aware Training for 8-Bit Neural Network Accelerator with On-Device Speech Recognition.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ICML - aware Differentiation for Improved Quantization-aware Training
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment.
- [Ocean Engineering
- [EANN - Aware Training Method for Photonic Neural Networks.
- [CCF Transactions on High Performance Computing
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs.
- [ECCV - Aware Dynamic Quantization for Image Super-Resolution
- [ECCV - Adaptive Quantization-Aware Neural Network Training: A Meta-Learning Approach [[code](https://github.com/jsjs0369/MEBQAT)] [](https://github.com/jsjs0369/MEBQAT)
- [ECCV - grained Data Distribution Alignment for Post-Training Quantization [[code](https://github.com/zysxmu/FDDA)] [](https://github.com/zysxmu/FDDA)
- [CCF Transactions on High Performance Computing
- [EANN - Aware Training Method for Photonic Neural Networks
- [ECCV - Computing-Lab-Yale/NDA_SNN)
- [ESE - Based Software Testing approach for Deep Neural Network Quantization assessment
- [Intelligent Automation & Soft Computing - Efficient Convolutional Neural Network Accelerator Using Fine-Grained Logarithmic Quantization
- [LNAI - Driven Quantization for Low-Bit and Sparse DNNs
-
2023
- [CVPRW - hoan-le/binaryvit)]
- [EMNLP - FP4: 4-Bit Floating-Point Quantized Transformers [[code](https://github.com/nbasyl/LLM-FP4)]
- [arXiv - HyViT: Post-Training Quantization for Hybrid Vision Transformer with Bridge Block Reconstruction.
- [arXiv
- [arXiv - Diffusion: Vector Quantized Discrete Diffusion Model with Spiking Neural Networks [[code](https://github.com/Arktis2022/Spiking-Diffusion)] [__`snn`__]
- [arXiv - free Quantization for Diffusion Models
- [CVPR
- [ICML - Training Quantization for Large Language Models [[code](https://github.com/mit-han-lab/smoothquant)] [](https://github.com/mit-han-lab/smoothquant)
- [arXiv - GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language Models
- [ICLR - Training Quantization for Generative Pre-trained Transformers [[code](https://github.com/IST-DASLab/gptq)] [721⭐]
- [ICML - wise Division for Post-Training Quantization [[code](https://openreview.net/attachment?id=-tYCaP0phY_&name=supplementary_material)]
- [ICML
- [ACL - agnostic Quantization Approach for Pre-trained Language Models
- [arXiv
- [arXiv - based Post-training Quantization for Large Language Models. [[code](https://github.com/hahnyuan/RPTQ4LLM)]
- [arXiv - V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation.
- [arXiv - Bit Quantization on Large Language Models.
- [arXiv - Efficiency Trade-off of LLM Inference with Transferable Prompt
- [arXiv - aware Weight Quantization for LLM Compression and Acceleration [[code](https://github.com/mit-han-lab/llm-awq)]
- [arXiv - QAT: Data-Free Quantization Aware Training for Large Language Models
- [arXiv - Quantized Representation for Near-Lossless LLM Weight Compression [[code](https://github.com/Vahe1994/SpQR)]
- [arXiv
- [arXiv - and-Sparse Quantization [[code](https://github.com/SqueezeAILab/SqueezeLLM)]
- [arXiv - Tunable Quantized Large Language Models with Error Correction through Low-Rank Adaptation
- [arXiv - FP-QSim: Mixed Precision and Formats For Large Language Models and Vision Transformers [[code](https://github.com/lightmatter-ai/INT-FP-QSim)]
- [ICML - DASLab/QIGen)]
- [arXiv
- [arXiv - FP: A Leap Forward in LLMs Post-Training W4A8 Quantization Using Floating-Point Formats.
- [arXiv - Uniform Post-Training Quantization via Power Exponent Search
- [arXiv - Based Post-Training Quantization: Challenging the Status Quo
- [arXiv - Grained Weight-Only Quantization for LLMs
- [arXiv
- [arXiv - grained Post-Training Quantization for Large Language Models
- [arXiv - time Weight Clustering for Large Language Models
- [arXiv - based Quantization for Language Models - An Efficient and Intuitive Algorithm
- [arXiv - performance Low-bit Quantization of Large Language Models
- [arXiv - Training Quantization on Large Language Models
- [arXiv - VQ: Compression for Tractable Internet-Scale Memory
- [arXiv - compressor)]
- [arXiv - training Quantization with FP8 Formats [[code](https://github.com/intel/neural-compressor)]
- [arXiv - LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models [[code](https://github.com/yuhuixu1993/qa-lora)]
- [arXiv - bit Weight Quantization of Large Language Models
- [arXiv - Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers
- [arXiv - LLM: Partially Binarized Large Language Models. [[code](https://github.com/hahnyuan/PB-LLM)]
- [arXiv - Bitwidth Quantization for Large Language Models
- [arXiv - Fine-Tuning-Aware Quantization for Large Language Models [[code](https://github.com/yxli2123/LoftQ)]
- [arXiv - parameter Tuning of LLMs with Affordable Resources
- [arXiv - compressor)]
- [arXiv - bit Transformers for Large Language Models [[code](https://github.com/kyegomez/BitNet)]
- [arXiv - LM: Training FP8 Large Language Models [[code](https://github.com/Azure/MS-AMP)]
- [arXiv
- [arXiv - Training Quantization with Activation-Weight Equalization for Large Language Models
- [arXiv - bit Quantization for Efficient and Accurate LLM Serving [[code](https://github.com/efeslab/Atom)]
- [arXiv - 1-Bit Compression of Trillion-Parameter Models
- [EMNLP - Shot Sharpness-Aware Quantization for Pre-trained Language Models
- [EMNLP - based Quantisation: What is Important for Sub-8-bit LLM Inference?
- [EMNLP
- [EMNLP - Watermark)]
- [NeurIPS
- [NeurIPS - bit Quantization for Efficient Image Super-Resolution [[code](https://github.com/htqin/QuantSR)]
- [NeurIPS
- [NeurIPS
- [NeurIPS - DM: An Efficient Low-bit Quantized Diffusion Model
- [NeurIPS - Training Quantization for Diffusion Models [[code](https://github.com/ziplab/PTQD)]
- [NeurIPS
- [NeurIPS - Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantization
- [ICML
- [ICML - Zip: Deep Compression of Finetuned Large Language Models
- [ICML - bit precision: k-bit Inference Scaling Laws
- [TPAMI - Based Post-Training Quantization With Bit-Split and Stitching
- [TPAMI - path Bit Sharing for Automatic Loss-aware Model Compression
- [ICCV - DFQ: Causality Guided Data-free Network Quantization [[code](https://github.com/42Shawn/Causal-DFQ)]
- [ICCV - Diffusion: Quantizing Diffusion Models [[code](https://github.com/Xiuyu-Li/q-diffusion)]
- [ICCV - BEV : Quantization-aware View-guided Distillation for Multi-view 3D Object Detection
- [CVPR - training Quantization on Diffusion Models [[code](https://github.com/42Shawn/PTQ4DM)]
- [CVPR - DETR: An Efficient Low-Bit Quantized Detection Transformer [[code](https://github.com/SteveTsui/Q-DETR)]
- [CVPR - Shot Quantization
- [CVPR - Training Quantization for Image Super Resolution
- [CVPR - Shot Model for Mixed-Precision Quantization
- [CVPR - Quant: Post-Training Quantization Based on Prediction Difference Metric [[code](https://github.com/hustvl/PD-Quant)]
- [CVPR - Free Quantization
- [CVPR - Enhanced Post-Training Activation Quantization for Vision Transformers
- [CVPR - Friendly Sparsity and Quantization
- [CVPR - based Integrated Pseudo-Quantization
- [CVPR - shrinking: Limiting Instantaneous Sharpness for Improving Post-training Quantization
- [CVPR - Training Quantization Through a Theoretical Perspective
- [CVPR - quantization
- [CVPR
- [ICLR - Conditioning
- [ACL - based Language Models with GPU-Friendly Sparsity and Quantization
- [TNNLS - Network Performance. [__`bnn`__] [[code](https://github.com/htqin/BiFSMNv2)]
- [TNNLS
- [HPCA - Quality Uncertainty Quantification in a PIM Designed for Bayesian Neural Network
- [TIP - Bitwidth-Fixed, Mixed-Precision Quantization Method for Mobile CNN-Based Applications
- [TCSVT
- [WACV
- [WACV - Free Per-Channel Static Input Quantization
- [WACV - Teacher Knowledge Distillation for Learning Low Bit-width Deep Neural Networks.
- [PR
- [PR - free quantization via mixed-precision compensation without fine-tuning.
- [NN - range zero-shot generative deep network quantization.
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [arXiv - training Quantization for Neural Networks with Provable Guarantees.
- [arXiv - Resolution. [__`bnn`__]
- [arXiv
- [arXiv - Training Quantization on Object Detection with Task Loss-Guided Lp Metric. [__`ptq`__]
- [arXiv - bit Integers [[code](https://github.com/xijiu9/Train_Transformers_with_INT4)]
- [arXiv
- [arXiv - Bit Quantization of Large Language Models With Guarantees. [[code](https://github.com/jerry-chee/QuIP)]
- [arXiv - Scaled Logit Distillation for Ternary Weight Generative Language Models
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [ICCV
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [arXiv
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [ISCA - friendly Outlier-Victim Pair Quantization
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [arXiv
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [ICCV - Free Compression: Pruning and Quantization without Fine-Tuning
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [TNNLS - Network Performance. [__`bnn`__] [[code](https://github.com/htqin/BiFSMNv2)]
- [HPCA - Quality Uncertainty Quantification in a PIM Designed for Bayesian Neural Network
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [ICML
- [CVPR - Enhanced Post-Training Activation Quantization for Vision Transformers
- [CVPR - Training Quantization Through a Theoretical Perspective
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [arXiv - training Quantization for Neural Networks with Provable Guarantees.
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [Cognitive Neurodynamics - based convolutional neural network.
- [MMM
- [MMM
- [MMM
- [MMM
- [MMM
- [MMM
- [MMM
- [MMM
- [MMM
- [MMM
- [MMM
- [MMM
- [MMM
- [PR - free quantization via mixed-precision compensation without fine-tuning.
- [MMM
- [MMM
- [MMM
- [MMM
- [MMM
- [MMM
- [MMM
- [MMM
- [MMM
- [ICCV - diffusion: Quantizing Diffusion Models [[code](https://github.com/Xiuyu-Li/q-diffusion)] [](https://github.com/Xiuyu-Li/q-diffusion)
- [MMM
- [NN - range zero-shot generative deep network quantization.
- [MMM
- [NeurIPS - bit Quantization for Efficient Image Super-Resolution [[code](https://github.com/htqin/QuantSR)] [](https://github.com/htqin/QuantSR)
- [TPAMI - Free Quantization [[code](https://github.com/htqin/DSG)] [](https://github.com/htqin/DSG)
- [AAAI - Width Reshaping
- [AAAI
- [AAAI
- [AAAI
- [AAAI - Free Quantization as a Zero-Sum Game
- [CVPR - Enhanced Post-Training Activation Quantization for Vision Transformers
- [CVPR - training Quantization on Diffusion Models [[code](https://https//github.com/42Shawn/PTQ4DM)]
- [CVPR - DETR: An Efficient Low-Bit Quantized Detection Transformer [[code](https://github.com/SteveTsui/Q-DETR)] [](https://github.com/SteveTsui/Q-DETR)
- [ICCV - Aware Quantization with Guaranteed Overflow Avoidance
- [ICCV
- [ICCV - Bit Power-of-Two Quantization
- [ICCV - free Proxies for Automated Mixed Precision Quantization
- [ICCV - Net: Elastic Quantization Neural Networks [[code](https://github.com/xuke225/EQ-Net)] [](https://github.com/xuke225/EQ-Net)
- [ICCV - ViT: Integer-only Quantization for Efficient Vision Transformer Inference [[code](https://github.com/zkkli/I-ViT)] [](https://github.com/zkkli/I-ViT)
- [ICCV
- [ICCV - Aware Training
- [ICCV - BEV: Quantization-aware View-guided Distillation for Multi-view 3D Object Detection
- [ICCV - ViT: Scale Reparameterization for Post-Training Quantization of Vision Transformers [[code](https://github.com/zkkli/RepQ-ViT)] [](https://github.com/zkkli/RepQ-ViT)
- [ICML - bit Backward: Quantized Gradients of Activation Functions for Memory Footprint Reduction [[code](https://github.com/SkoltechAI/fewbit)] [](https://github.com/SkoltechAI/fewbit)
- [ICML - wise Division for Post-Training Quantization [[code](https://openreview.net/attachment?id=-tYCaP0phY_&name=supplementary_material)]
- [ICML - free Quantization for Low-bit Vision Transformers [[code](https://github.com/nbasyl/OFQ)] [](https://github.com/nbasyl/OFQ)
- [ICML
- [ICML - bit precision: k-bit Inference Scaling Laws
- [ICML
- [NeurIPS - Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantization
- [NeurIPS - 8-bit Vision Transformers via Full and Packed Quantization on the Mobile
- [NeurIPS
- [NeurIPS - Bit Quantization of Large Language Models With Guarantees [[code](https://github.com/jerry-chee/QuIP)] [](https://github.com/jerry-chee/QuIP)
- [NeurIPS
- [NeurIPS - shot Network Quantization with Texture Feature Distribution Calibration
- [NeurIPS
- [arXiv - HERO: Hardware-Enhanced Robust Optimized Post-Training Quantization Framework for W8A8 Transformers
- [AAAI - Aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural Networks [[code](https://github.com/mlech26l/quantization_aware_ibp)] [](https://github.com/mlech26l/quantization_aware_ibp)
- [ICLR - Uniform Quantization
- [ICLR - Scaling Floating-Point (BSFP) : An Efficient Non-Uniform Quantization For Low Precision Inference
- [NeurIPS - Free Residual Quantization Error Expansion
- [NeurIPS
- [NeurIPS
- [NeurIPS - AI-research/pruning-vs-quantization)] [](https://github.com/Qualcomm-AI-research/pruning-vs-quantization)
- [ICLR - Aware Quantization for Graph Neural Networks
-
2019
- [NeurIPS
- [AAAI
- [AAAI - bit CNNs via Discrete Back Propagation. [**`bnn`**]
- [APCCAS
- [BMVC - Net++: Improved Binary Neural Networks. [**`bnn`**]
- [BMVC
- [CoRR - bit DCNNs. [**`bnn`**]
- [CoRR - Ensemble Template for Accurate Binary Convolutional Neural Networks. [**`bnn`**]
- [CoRR
- [CoRR
- [CoRR
- [CoRR - xnor/BMXNet-v2)] [193:star:]
- [CVPR
- [CVPR - Map Sparsity Through Low-Bit Quantization. [**`qnn`**]
- [CVPR - Aware Automated Quantization with Mixed Precision. [**`qnn`**] [**`hardware`**] [[torch](https://github.com/mit-han-lab/haq)] [233:star:]
- [CVPR - quantization-networks)] [82:star:]
- [CVPR
- [CVPR - Wise Interactions for Binary Convolutional Neural Networks. [**`bnn`**]
- [CVPR - bit DCNNs with Circulant Back Propagation. [**`bnn`**]
- [CVPR
- [CVPR
- [CVPR
- [FPGA - Efficient Binarized Neural Network Inference on FPGA. [**`bnn`**] [**`hardware`**]
- [ICCV - Precision and Low-Bit Neural Networks. [**`qnn`**]
- [ICCV - bit cnns. [**`bnn`**]
- [ICCV
- [ICCV - Precision. [**`qnn`**]
- [ICCV - Free Quantization Through Weight Equalization and Bias Correction. [**`qnn`**] [**`hardware`**] [[torch](https://github.com/jakc4103/DFQ)]
- [ICCV
- [ICML - Bit Quantization of Transformer Neural Machine Language Translation Model. [**`qnn`**] [**`nlp`**]
- [ICLR
- [ICLR
- [ICIP - xnor/BMXNet-v2)] [192:star:]
- [ICUS
- [IJCAI - Efficient Hashing with Minimizing Quantization Loss. [**`bnn`**]
- [IJCAI
- [ISOCC
- [IEEE J. Emerg. Sel. Topics Circuits Syst. - Chip Systolically Scalable Binary-Weight CNN Inference Engine. [**`hardware`**]
- [IEEE JETC
- [IEEE J. Solid-State Circuits - Efficient Reconfigurable Processor for Binary-and Ternary-Weight Neural Networks With Flexible Data Bit Width. [**`qnn`**]
- [MDPI Electronics
- [NeurIPS - differentiable Quantization. [**`qnn`**] [[torch](https://github.com/csyhhu/MetaQuant)]
- [NeurIPS - bnn-optimization)]
- [NeurIPS
- [NeurIPS
- [NeurIPS
- [NeurIPS
- [RoEduNet
- [SiPS
- [TMM - Modal Hashing. [**`bnn`**]
- [TMM
- [IEEE TCS.I - RAM: Accelerating Binary Neural Networks in High-Throughput SRAM Compute Arrays. [**`hardware`**]
- [IEEE TCS.I - Chip Memory. [**`bnn`**]
- [VLSI-SoC - Efficient Execution of Binary Neural Networks Using Resistive Memories. [**`bnn`**] [**`hardware`**]
- [paper
- [arXiv - Binarizing Networks. [**`bnn`**]
- [arXiv
- [arXiv - CV/dabnn)]
- [arXiv - aware Knowledge Distillation. [**`qnn`**]
- [arXiv
- [CVPR
- [AAAI
- [AAAI - bit CNNs via Discrete Back Propagation. [**`bnn`**]
- [APCCAS
- [CoRR
- [GLSVLSI
- [ICCV - bit cnns. [**`bnn`**]
- [ICUS
- [IEEE J. Emerg. Sel. Topics Circuits Syst. - Chip Systolically Scalable Binary-Weight CNN Inference Engine. [**`hardware`**]
- [IEEE JETC
- [NeurIPS
- [RoEduNet
- [TMM
- [VLSI-SoC - Efficient Execution of Binary Neural Networks Using Resistive Memories. [**`bnn`**] [**`hardware`**]
- [arXiv - CV/dabnn)]
- [MDPI Electronics
-
2015
-
2024
- [arXiv
- [ICLR
- [arXiv - Efficient Tuning of Quantized Large Language Models
- [arXiv - LLM: Efficiently Serving Large Language Models Through FP6-Centric Algorithm-System Co-Design
- [arXiv
- [ICLR
- [arXiv - Aware Training on Large Language Models via LoRA-wise LSQ
- [arXiv - Bit Quantized Large Language Model
- [NeurIPS - Level Post-Training Quantization of Hyper-Scale Transformers [[code](https://github.com/SamsungLabs/aespa)] [](https://github.com/SamsungLabs/aespa)
- [arXiv - Aware Training for the Acceleration of Lightweight LLMs on the Edge [[code](https://github.com/shawnricecake/EdgeQAT)] 
- [arXiv - Precision LLM: Low-Cost Deployment of Multiple, Different-Sized LLMs
- [arXiv - Rank Quantization Error Reconstruction for LLMs
- [arXiv - Free Asymmetric 2bit Quantization for KV Cache [[code](https://github.com/jy-yuan/KIVI)] 
- [arXiv - Training Quantization for LLMs [[code](https://github.com/Aaronhuang-778/BiLLM)]
- [arXiv - RelaxML/quip-sharp)] 
- [arXiv - Aware Dequantization
- [arXiv - Finetuning Quantization of LLMs via Information Retention [[code](https://github.com/htqin/IR-QLoRA)]
- [arXiv - 4-Bit LLMs via Self-Distillation [[code](https://github.com/DD-DuDa/BitDistiller)] 
- [arXiv - bit Large Language Models
- [arXiv - LLM: Accurate Dual-Binarization for Efficient LLMs
- [arXiv
- [DAC
- [arXiv - Aware Mixed Precision Quantization
- [arXiv - bound for Large Language Models with Per-tensor Quantization
- [arXiv - ai-research/gptvq)] 
- [DAC - aware Post-Training Mixed-Precision Quantization for Large Language Models
- [arXiv
- [arXiv - PQ: Serving LLM on Heterogeneous Clusters with Phase-Aware Partition and Adaptive Quantization
- [arXiv
- [arXiv
- [arXiv - free Quantization Algorithm for LLMs
- [arXiv - KVCacheQuantization)] 
- [arXiv
- [arXiv - Lossless Generative Inference of LLM
- [arXiv - LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression [[code](https://github.com/AIoT-MLSys-Lab/SVD-LLM)] 
- [ICLR Practical ML for Low Resource Settings Workshop
- [arXiv
- [arXiv
- [arXiv - Free 4-Bit Inference in Rotated LLMs [[code](https://github.com/spcl/QuaRot)] 
- [arXiv - compensation)] 
- [arXiv - Zheng/BinaryDM)]
- [arXiv - chip Hardware-aware Quantization
- [arXiv - bit Quantized LLaMA3 Models? An Empirical Study [[code](https://github.com/Macaronlin/LLaMA3-Quantization)] [[HuggingFace](https://huggingface.co/LLMQ)]
- [ICML - distributions to explore accurate and efficient formats for llms [[code](https://github.com/cornell-zhang/llm-datatypes)] [](https://github.com/cornell-zhang/llm-datatypes)
- [arXiv - Training Quantization with Low-precision Minifloats and Integers on FPGAs [[code](https://github.com/Xilinx/brevitas/tree/dev/src/brevitas_examples/imagenet_classification/ptq)][__`hardware`__]
- [NeurIPS
- [NeurIPS - Training Quantization [[code](https://github.com/aozhongzhang/magr)] [](https://github.com/aozhongzhang/magr)
- [NeurIPS - RelaxML/qtip)] [](https://github.com/Cornell-RelaxML/qtip)
- [CVPR - Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devices
- [NeurIPS - bit Communication Quantization in Sharded Data Parallelism for LLM Training [[code](https://github.com/ByteDance-Seed/SDP4Bit)] [](https://github.com/ByteDance-Seed/SDP4Bit)
- [TMLR - Sparsity Trade-Off [[code](https://github.com/sachitkuhar/PLUM)][[webpage](https://github.com/sachitkuhar/PLUM)][[video](https://www.youtube.com/watch?v=nE_CYDWqQ_I)][**`bnn`**] [**`inference`**]
- [ICML - Block Quantization
- [ICML - Training Quantization for LLMs [[code](https://github.com/Aaronhuang-778/BiLLM)] [](https://github.com/Aaronhuang-778/BiLLM)
- [ICML
- [NeurIPS
- [ACL Findings - LLM: Accurate Dual-Binarization for Efficient LLMs
- [NeurIPS - Resolution [[code](https://github.com/zhengchen1999/BI-DiffSR)] [](https://github.com/zhengchen1999/BI-DiffSR)
- [NeurIPS - bit Post-Training Quantization for Image Super-Resolution [[code](https://github.com/Kai-Liu001/2DQuant)] [](https://github.com/Kai-Liu001/2DQuant)
- [ICML - Finetuning Quantization of LLMs via Information Retention [[code](https://github.com/htqin/IR-QLoRA)] [](https://github.com/htqin/IR-QLoRA)
- [ICML - Resolution
- [AAAI - Quant: Activation-Guided Quantization for Faster Inference of LLMs on the Edge
- [AAAI - DETR: Low-Bit Quantized Detection Transformer with Auxiliary Queries
- [AAAI - ViT: Pushing the Limit of Vision Transformer Quantization
- [AAAI - training Quantization in LLMs from Comprehensive Study to Low Rank Compensation
- [AAAI - Aware Approach
- [AAAI - State Precision Searcher for Mixed-Precision Activation Quantization
- [AAAI - Performance Low-Bit Quantization of Large Language Models
- [AAAI - Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models
- [AAAI - training Multi-Bit Quantization of Neural Networks
- [AAAI - Guided Image Synthesis for Data-Free Quantization
- [AAAI
- [ACL
- [ACM MM - Aware Scale Learning Based on Warmup
- [CVPR - Free Quantization via Pseudo-label Filtering
- [CVPR - training Quantization Calibration through Contrastive Learning
- [CVPR - Aware Group Quantization for Vision Transformers
- [CVPR - Training Quantization for Segment Anything
- [CVPR - PTQ: Regression-specialized Post-training Quantization for Fully Quantized Object Detector
- [CVPR - Free Model Quantization via One-Shot Weight-Coupling Learning
- [CVPR - DM: Temporal Feature Maintenance Quantization for Diffusion Models
- [CVPR - training Quantization for Diffusion Models
- [ECCV - Training Quantization for Vision Transformers with Adaptive Logarithm Quantizer
- [ECCV - ViT: Contrastive Data-Free Learning for Adaptive Post-Training Quantization of ViTs
- [ECCV - Efficient Fine-Tuning for Quantized Diffusion Model
- [ECCV - Data Augmentation for Post-Training Quantization
- [ECCV - Efficient Few-Step Text-to-Image Diffusion Models with Metric-Decoupled Mixed Precision Quantization
- [ECCV - Resolution Networks
- [ECCV - training Quantization with Progressive Calibration and Activation Relaxing for Text-to-Image Diffusion Models
- [ECCV - SAM: Post-training Quantization for Segment Anything Model
- [ECCV - Aware Correction for Quantized Diffusion Models
- [ECCV - bit Quantization of Super Resolution Networks
- [EMNLP - Bit Quantized Large Language Model
- [EMNLP
- [EMNLP - bit Vector Post-Training Quantization for Large Language Models
- [ICLR - Aware Fine-Tuning of Low-Bit Diffusion Models
- [ICLR - PTQ: Post-Training Quantization for Point Cloud 3D Object Detection
- [ICLR - Fine-Tuning-aware Quantization for Large Language Models [[code](https://github.com/yxli2123/LoftQ)] [](https://github.com/yxli2123/LoftQ)
- [ICLR - GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language Models
- [ICLR
- [ICLR - LLM: Partially Binarized Large Language Models [[code](https://github.com/hahnyuan/PB-LLM)] [](https://github.com/hahnyuan/PB-LLM)
- [ICLR - LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models [[code](https://github.com/yuhuixu1993/qa-lora)] [](https://github.com/yuhuixu1993/qa-lora)
- [ICLR - Bitwidth Quantization for Large Language Models
- [ICLR - bit Weight Quantization of Large Language Models
- [ICLR - Quantized Representation for Near-Lossless LLM Weight Compression [[code](https://github.com/Vahe1994/SpQR)] [](https://github.com/Vahe1994/SpQR)
- [ICML - Aware Weight Quantization
- [ICML - Exponent Block Floating-Point for Large Language Models Quantization
- [ICML - Training Quantization of Vision Transformers
- [ICML
- [ICML
- [ICML - Bit Quantization for Transformers
- [ICML - Free Asymmetric 2bit Quantization for KV Cache [[code](https://github.com/jy-yuan/KIVI)] [](https://github.com/jy-yuan/KIVI)
- [ICML - Rank Quantization Error Reconstruction for LLMs
- [ICML - aware Slicing for Post-Training Quantization in Vision Transformer
- [ICML - Aware Data Generation for Zero-shot Quantization
- [ICML - and-Sparse Quantization [[code](https://github.com/SqueezeAILab/SqueezeLLM)] [](https://github.com/SqueezeAILab/SqueezeLLM)
- [MLSys - aware Weight Quantization for On-Device LLM Compression and Acceleration [[code](https://github.com/mit-han-lab/llm-awq)] [](https://github.com/mit-han-lab/llm-awq)
- [NeurIPS
- [NeurIPS
- [NeurIPS
- [NeurIPS
- [NeurIPS - training Quantization for Diffusion Transformers
- [NeurIPS - VLM: Post-training Quantization for Large Vision-Language Models
- [NeurIPS
- [NeurIPS
- [ACL Findings
- [ACL Findings
- [ACL Findings - QAT: Data-Free Quantization Aware Training for Large Language Models
- [EMNLP Findings - Activation Quantization of LLMs
- [EMNLP Findings - tuning Rotated Outlier-free LLMs for Effective Weight-Activation Quantization
- [EMNLP Findings
- [EMNLP Findings - friendly Quantization for On-device Language Models
- [EMNLP Findings - Tuning of LLMs
- [arXiv - Training Quantization of Large Transformer Models via Scale Reparameterization
- [SIGMOD - Dimensional Vectors with a Theoretical Error Bound for Approximate Nearest Neighbor Search [[code](https://github.com/gaoj0017/RaBitQ)] [](https://github.com/gaoj0017/RaBitQ)
- [AAAI - Step Forward and Backtrack: Overcoming Zig-Zagging in Loss-Aware Quantization Training
- [ICML - aware Training for In-memory Computing Systems
- [NeurIPS - task LLM Quantization and Serving for Multiple LoRA Adapters
- [NeurIPS - Lab-Cornell/QuantNets)] [](https://github.com/Grosenick-Lab-Cornell/QuantNets)
- [NeurIPS
- [NeurIPS
-
2025
- [ICML - aware Post-training Quantization without Backpropagation
- [ICML - shot Quantization and Sparsity with Low-rank Approximation for LLM Weight Compression [[code](https://github.com/Paramathic/slim)] [](https://github.com/Paramathic/slim)
- [ICML - thread INT4 Quantization [[code](https://github.com/thu-ml/SageAttention)] [](https://github.com/thu-ml/SageAttention)
- [ICML - bit Quantization Pushing The Limits of Post-Training Quantization
- [ICML
- [ICCV - Compatible Post-Training Quantization for Segment Anything Model
- [ICML - Free Quantization with Asymmetric Calibration [[code](https://github.com/Intelligent-Computing-Lab-Panda/GPTAQ)] [](https://github.com/Intelligent-Computing-Lab-Panda/GPTAQ)
- [ICML - training Quantization Framework for Selective State Space Models [[code](https://github.com/enyac-group/Quamba)] [](https://github.com/enyac-group/Quamba)
- [NeurIPS - Wise Post-Training Quantization
- [ICML - of-Experts Large Language Models via Expert-Balanced Sampling and Affinity Guidance [[code](https://github.com/chenzx921020/MoEQuant)] [](https://github.com/chenzx921020/MoEQuant)
- [NeurIPS - QAF: Lossless Ternary Adaptation for Quantization-Aware Fine-Tuning [[code](https://github.com/KingdalfGoodman/LoTA-QAF/blob/main/README.md)] [](https://github.com/KingdalfGoodman/LoTA-QAF)
- [ICCV - Guided Quantization with Hierarchical Latent and Layer Caching for Video Generation [[code](https://github.com/JunyiWuCode/QuantCache)] [](https://github.com/JunyiWuCode/QuantCache)
- [ICML
- [ICCV - bit Diffusion Model Quantization via Efficient Selective Finetuning [[code](https://github.com/hatchetProject/QuEST)] [](https://github.com/hatchetProject/QuEST)
- [ICCV - Training Quantization [[code](https://github.com/LeeDongYeun/dmq)] [](https://github.com/LeeDongYeun/dmq)
- [AAAI - Aware Loss
- [ICML - VDiT: Towards Accurate Quantization and Distillation of Video-Generation Diffusion Transformers [[code](https://github.com/cantbebetter2/Q-VDiT)] [](https://github.com/cantbebetter2/Q-VDiT)
- [AAAI - DM: Mixed Precision Quantization for Extremely Low Bit Diffusion Models
- [ICML - LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models [[code](https://github.com/Aaronhuang-778/SliM-LLM)] [](https://github.com/Aaronhuang-778/SliM-LLM)
- [TPAMI
- [NeurIPS - VDiT: Accurate Quantized Video Diffusion Transformer with Salient Data and Sparse Token Distillation [[code](https://github.com/wlfeng0509/S2Q-VDiT)] [](https://github.com/wlfeng0509/S2Q-VDiT)
- [CVPR - Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution [[code](https://github.com/libozhu03/PassionSR)] [](https://github.com/libozhu03/PassionSR)
- [ICLR - LLM: Alternating Refined Binarizations for Large Language Models [[code](https://github.com/ZHITENGLI/ARB-LLM)] [](https://github.com/ZHITENGLI/ARB-LLM)
- [ICLR - Zheng/BinaryDM)] [](https://github.com/Xingyu-Zheng/BinaryDM)
- [ICML
- [ICML - Aware Supervised Fine-Tuning Approach for Large Language Models [[code](https://github.com/OptimAI-Lab/RoSTE)] [](https://github.com/OptimAI-Lab/RoSTE)
- [ICML - Adaptive Non-Uniform Quantization for Large Language Models
- [ICML
- [NeurIPS - CLab/DartQuant)] [](https://github.com/CAS-CLab/DartQuant)
- [AAAI - Bit Quantization
- [AAAI - adaptive Calibration for Accurate Post-Training Quantization of LLMs
- [AAAI
- [AAAI - DM: Timestep-Channel Adaptive Quantization for Diffusion Models
- [ACL - Aware Training for Large Language Models [[code](https://github.com/OpenGVLab/EfficientQAT)] [](https://github.com/OpenGVLab/EfficientQAT)
- [ACL - Aware Fine-Tuning on Large Language Models
- [ACL - Precision Quantization for Long-Context LLM Inference via Mixture of Quantization-Aware Experts
- [ACL - Safe Pre-Training for Robust 4-Bit Quantization of Large Language Models
- [ACL - Bit Post-Training Quantization Methods for Large Language Models [[code](https://github.com/zjq0455/PTQ1.61)] [](https://github.com/zjq0455/PTQ1.61)
- [ACL - coding Quantization for Accurate Compression of Large Language Models
- [ACL - Performance Trade-Offs in LLM Quantization
- [ACM MM - Aware Training for Diffusion Models via Weight Dilation
- [ACM MM - positive Distillation
- [ACM MM - Training Quantization
- [ACM MM
- [EMNLP - precision Weight-Only Quantization of Large Language Models
- [EMNLP - input and long-output tasks?
- [ICLR - Block Quantization for Large Language Models
- [ICLR - Aware Group Quantization for Text-to-Image Diffusion Models
- [ICLR - error-aware Grid
- [ICLR
- [ICLR - 98/QERA)] [](https://github.com/ChengZhang-98/QERA)
- [ICLR
- [ICLR - Rank Component for 4-Bit Diffusion Models
- [ICLR - Q: Efficient and Accurate Quantization of Diffusion Transformers for Image and Video Generation
- [ICML - mllab/GuidedQuant)] [](https://github.com/snu-mllab/GuidedQuant)
- [ICML - Precision Quantization of Large Language Models with Low-Rank Residuals [[code](https://github.com/utkarsh-dmx/project-resq)] [](https://github.com/utkarsh-dmx/project-resq)
- [NeurIPS - Free Low-Bit KV Cache Quantization
- [NeurIPS - Order Quantization for Real-Valued Matrix Compression
- [NeurIPS - Bit Large Language Models
- [NeurIPS - Bit Quantization via Latent Factorization
- [NeurIPS - bit LLM Quantization
- [NeurIPS - Palette: Fractional-Bit Quantizers Toward Optimal Weight-Only Post-Training Quantization
- [NeurIPS - Enhanced High-Fidelity 1-Bit Quantization for LLMs
- [ACL Findings - Training Quantization
- [EMNLP Findings - based LLM Quantization
- [SIGMOD - Dimensional Vectors in Euclidean Space for Approximate Nearest Neighbor Search [[code](https://github.com/VectorDB-NTU/Extended-RaBitQ)] [](https://github.com/VectorDB-NTU/Extended-RaBitQ)
- [NeurIPS - Resolution
- [NeurIPS - bit Quantization for Point Cloud 3D Detection
- [NeurIPS - VE: Progressive Multi-Frame Quantization for Video Enhancement [[code](https://github.com/xiaoBIGfeng/PMQ-VE)] [](https://github.com/xiaoBIGfeng/PMQ-VE)
- [NeurIPS - DiT: Variance-Equalized and Temporally Adaptive Quantization for Efficient 4-bit Diffusion Transformers
- [NeurIPS - bit Quantization Network Training via Weight Bias Correction and Bit-wise Coreset Sampling
- [NeurIPS - Precision Quantization via Topological Entropy
- [NeurIPS - Compensating Auxiliary for Monocular Depth Estimation
- [ICCV - Aware Training [[code](https://github.com/cvlab-yonsei/TRS)] [](https://github.com/cvlab-yonsei/TRS)
- [ICCV - Specific Zero-shot Quantization-Aware Training for Object Detection [[code](https://github.com/DFQ-Dojo/dfq-toolkit)] [](https://github.com/DFQ-Dojo/dfq-toolkit)
- [ICCV - Free Quantization Framework for Vision Mamba
- [ICCV - Aware Non-Uniform Quantization [[code](https://github.com/Seongyeol-kim/FedWSQ)] [](https://github.com/Seongyeol-kim/FedWSQ)
- [ICCV - Free Quantization of Vision Transformers [[code](https://github.com/zysxmu/SARDFQ)] [](https://github.com/zysxmu/SARDFQ)
- [ICCV - Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective
- [ICCV - Efficient Bit Sparsification Quantization
- [ICML - precision Quantization for MoE with Accuracy and Performance Co-Design [[code](https://github.com/cat538/MxMoE)] [](https://github.com/cat538/MxMoE)
- [ICML - Precision Quantization via Adaptive Sharpness-Aware Gradient Aligning
- [ICML - Affine Regularized Quantization [[code](https://github.com/facebookresearch/parq)] [](https://github.com/facebookresearch/parq)
- [ICML - QViT: Integrating Low-Rank Approximation and Quantization for Robust and Efficient Vision Transformers
- [ICML - resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models [[code](https://github.com/Thecommonirin/Qresafe)] [](https://github.com/Thecommonirin/Qresafe)
- [ICML - DoG: Quantization-Aware Training for Domain Generalization [[code](https://github.com/saqibjaved1/QT-DoG)] [](https://github.com/saqibjaved1/QT-DoG)
- [ICML
- [ICML - Friendly Post-Training Quantization for Multi-Target Domain Adaptation [[code](https://github.com/ewsn1593/HDRQ)] [](https://github.com/ewsn1593/HDRQ)
- [ICML - wise Quantization for Quantized Optimistic Dual Averaging
- [ICML - Aware Post-Training Quantization for Discrete Graph Diffusion Models
- [ICML - wise Fine-grained Mixed Format Quantization for Energy-Efficient LLM Inference
- [AAAI - Resolution by Intriguing Multi-Granularity Clues [[code](https://github.com/MmmingS/Granular-DQ)] [](https://github.com/MmmingS/Granular-DQ)
- [AAAI - DPM: Dual Denoising for Quantized Diffusion Probabilistic Models [[code](https://github.com/TaylorJocelyn/D2-DPM)] [](https://github.com/TaylorJocelyn/D2-DPM)
- [CVPR
- [CVPR - ViT: Post-Training Quantization with Average Perturbation Hessian Based Reconstruction for Vision Transformer [[code](https://github.com/GoatWu/APHQ-ViT)] [](https://github.com/GoatWu/APHQ-ViT)
- [ICLR - shot Quantization by Synthesis-aware Fine-tuning [[code](https://github.com/snudm-starlab/SynQ)] [](https://github.com/snudm-starlab/SynQ)
- [NeurIPS - Wise Post-Training Quantization [[Code](https://github.com/FujitsuResearch/OneCompression)] [](https://github.com/FujitsuResearch/OneCompression)
-
2026
- [CVPR Findings - MambaIR: Accurate Quantized Mamba for Efficient Image Restoration
- [ICLR - KVQ: Progressive Mixed-precision KV Cache Quantization for Long-CoT LLMs [[code](https://github.com/thu-nics/PM-KVQ)] [](https://github.com/thu-nics/PM-KVQ)
- [ICLR - Wise LLM Quantization by 4-bit Block-Wise Optimal Float (BOF4): Analysis and Variations [[code](https://github.com/ifnspaml/bof4)] [](https://github.com/ifnspaml/bof4)
- [ICLR - DASLab/FP-Quant)] [](https://github.com/IST-DASLab/FP-Quant)
- [AAAI - DQ: Time-Rotation Diffusion Quantization
- [ICLR - LLM: Post-Training Ternarization for Large Language Models [[code](https://github.com/XIANGLONGYAN/PT2-LLM)] [](https://github.com/XIANGLONGYAN/PT2-LLM)
- [ICLR - dLLM: Post-Training Extreme Low-Bit Quantization for Diffusion Large Language Models
- [ICLR - Quant: Data-free Video Diffusion Transformers Quantization
- [ICLR
- [ICLR
- [ICLR - Training Quantization for Video Matting
- [ICLR
- [ICLR
- [AAAI - Order Error Matters: Accurate Compensation for Quantized Large Language Models [[code](https://github.com/Xingyu-Zheng/FOEM)] [](https://github.com/Xingyu-Zheng/FOEM)
- [ICLR - optimal Distortion Rate
- [ICLR
- [ICLR - Coded Quantization for Multi-Precision LLMs [[code](https://github.com/naver-aics/anybcq)] [](https://github.com/naver-aics/anybcq)
- [ICLR - free Ternary Quantization for Large Language Models
- [ICLR - Training Quantization [[code](https://github.com/logart-lab/logart)] [](https://github.com/logart-lab/logart)
- [ICLR - lab/paroquant)] [](https://github.com/z-lab/paroquant)
- [ICLR - Wise LLM Quantization by 4-bit Generalized Normal Float Formats
- [arXiv - bit Post-Training Weight Quantization for LLMs
- [arXiv - Tuning Signals
- [arXiv - Training Quantization for LLMs
- [arXiv - Bit Quantization-Aware Training Work for Reasoning LLMs? A Systematic Study
- [ICLR - Aware Mixed-Precision Quantization for Efficient Long-Context Inference
- [ICLR - Precision Mixture-of-Experts
- [ICLR - Quantization-enhanced Reinforcement Learning for LLMs [[code](https://github.com/NVlabs/QeRL)] [](https://github.com/NVlabs/QeRL)
- [ICLR - Language-Action Model's Quantization
- [ICLR - bit Muon through subspace preservation and grid quantization
- [ICLR - and-Sum Quantization for Visual Autoregressive Models
- [ICLR - Centric Post-Training Quantization for Object Detection Models
- [ICLR - of-Experts with Theoretical Generalization Guarantees
- [ICLR
- [ICLR - Dimensional Linear Regression
- [ICLR - the-Fly Adaptation to Quantization: Configuration-Aware LoRA for Efficient Fine-Tuning of Quantized LLMs
- [ICLR - MoE: KLT-guided SVD with Bias-Corrected Vector Quantization for MoE Large Language Models [[code](https://github.com/xuzukang/kbvq_moe)] [](https://github.com/xuzukang/kbvq_moe)
- [ICLR - rank Compression for Adaptive Edge LLMs [[code](https://github.com/enyac-group/UniQL)] [](https://github.com/enyac-group/UniQL)
- [ICLR
- [ICLR
- [ICLR - point Quantization
- [ICLR - Training Quantization Robustness [[code](https://github.com/aldakata/TrainingDynamicsQuantizationRobustness)] [](https://github.com/aldakata/TrainingDynamicsQuantizationRobustness)
- [ICLR - bit Quantization for State Space Duality
- [ICLR
- [ICLR - Training Quantization for AutoRegressive Visual Generation Models [[code](https://github.com/BienLuky/PTQ4ARVG)] [](https://github.com/BienLuky/PTQ4ARVG)
- [ICLR - Aware Walsh-Hadamard Adaptation for Parameter-Efficient Fine-Tuning on Large Language Models [[code](https://github.com/vantaa89/qwha)] [](https://github.com/vantaa89/qwha)
- [ICLR - Aligned Calibration for Post-Training Quantization of Diffusion Models
- [ICLR - Aware Low-Rank Error Reconstruction for LLM Quantization
- [ICLR - Optimal Quantization-Aware Training
- [ICLR
- [ICLR - Training Quantization
- [ICLR - Precision Quantization with Microscaling Formats for Large Language Models [[code](https://github.com/lwy2020/MicroMix)] [](https://github.com/lwy2020/MicroMix)
- [ICLR - aware Quantization without Backpropagation
- [ICLR - Training Quantization of Diffusion Models
- [ICLR - based LLM Quantization
- [ICLR - based Rectifier Routing Quantization for Image Super-Resolution [[code](https://github.com/momo5-a11/SPR2Q)] [](https://github.com/momo5-a11/SPR2Q)
- [ICLR - Precision Activation Quantization
- [arXiv - Efficient 1.25-Bit Ternary Quantization via Fine-grained Sparsification [[code](https://github.com/Tencent/AngelSlim)] [](https://github.com/Tencent/AngelSlim)
- [arXiv - Bit Quantization-Aware Training Work for Reasoning LLMs? A Systematic Study
- [arXiv - Line Revolution for Generative AI Model Compression [[Code](https://github.com/FujitsuResearch/OneCompression)] [](https://github.com/FujitsuResearch/OneCompression)
-
-
Awesome_Efficient_LLM_Diffusion
- ![Awesome - ml/awesome-efficient-llm-diffusion)
-
Benchmark
-
BiBench
-
MQBench
-
-
Survey_Papers
-
Survey_of_Binarization
-
Survey_of_Quantization
-
-
Star History
-
2015
- ![Star History Chart - history.com/#Efficient-ML/Awesome-Model-Quantization&Timeline)
- ![Star History Chart - history.com/#Efficient-ML/Awesome-Model-Quantization&Timeline)
-
-
Survey Papers
-
Benchmarks
- [Paper
- [Paper - Quantization)] [](https://github.com/Macaronlin/LLaMA3-Quantization)
- [Paper - ML/Qwen3-Quantization)] [](https://github.com/Efficient-ML/Qwen3-Quantization)
- [Paper
- [Paper
-
Related Repositories
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