神经网络 Binary Neural Networks"/>
[综述] 二值神经网络 Binary Neural Networks
来源:
Survey_Papers
Survey_of_Binarization
Our survey paper Binary Neural Networks: A Survey (Pattern Recognition) is a comprehensive survey of recent progress in binary neural networks. For details, please refer to:
Binary Neural Networks: A Survey [Paper] [Blog]
Haotong Qin, Ruihao Gong, Xianglong Liu*, Xiao Bai, Jingkuan Song, and Nicu Sebe.
Survey_of_Quantization
The survey paper A Survey of Quantization Methods for Efficient Neural Network Inference (ArXiv) is a comprehensive survey of recent progress in quantization. For details, please refer to:
A Survey of Quantization Methods for Efficient Neural Network Inference [Paper]
Amir Gholami* , Sehoon Kim* , Zhen Dong* , Zhewei Yao* , Michael W. Mahoney, Kurt Keutzer. (* Equal contribution)
Benchmark
MQBench
The paper MQBench: Towards Reproducible and Deployable Model Quantization Benchmark (NeurIPS 2021) is a benchmark and framework for evluating the quantization algorithms under real world hardware deployments. For details, please refer to:
MQBench: Towards Reproducible and Deployable Model Quantization Benchmark [Paper] [Project]
Yuhang Li, Mingzhu Shen, Jian Ma, Yan Ren, Mingxin Zhao, Qi Zhang, Ruihao Gong, Fengwei Yu, Junjie Yan.
Papers
Keywords: qnn
: quantized neural networks | bnn
: binarized neural networks | hardware
: hardware deployment | snn
: spiking neural networks | other
Statistics: 🔥 highly cited | ⭐ code is available and star > 50
2022
- [IJCV] Distribution-sensitive Information Retention for Accurate Binary Neural Network. [
bnn
] - [IJCAI] BiFSMN: Binary Neural Network for Keyword Spotting. [
bnn
] [code] - [ICLR] BiBERT: Accurate Fully Binarized BERT. [
bnn
][code] - [CVPR] Implicit Feature Decoupling with Depthwise Quantization. [
qnn
] - [CVPR] Learnable Lookup Table for Neural Network Quantization. [
qnn
] - [CVPR] Mr.BiQ: Post-Training Non-Uniform Quantization based on Minimizing the Reconstruction Error. [
qnn
] - [CVPR] Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation. [
qnn
] - [CVPR] Data-Free Network Compression via Parametric Non-uniform Mixed Precision Quantization. [
qnn
] - [CVPR] Instance-Aware Dynamic Neural Network Quantization. [
qnn
] - [IJCAI] RAPQ: Rescuing Accuracy for Power-of-Two Low-bit Post-training Quantization. [
qnn
] - [IJCAI] MultiQuant: Training Once for Multi-bit Quantization of Neural Networks. [
qnn
] - [NeurIPS] Leveraging Inter-Layer Dependency for Post -Training Quantization. [
qnn
] - [NeurIPS] Theoretically Better and Numerically Faster Distributed Optimization with Smoothness-Aware Quantization Techniques. [
qnn
] - [NeurIPS] Entropy-Driven Mixed-Precision Quantization for Deep Network Design. [
qnn
] - [NeurIPS] Redistribution of Weights and Activations for AdderNet Quantization. [
qnn
] - [NeurIPS] FP8 Quantization: The Power of the Exponent. [
qnn
] - [NeurIPS] Towards Efficient Post-training Quantization of Pre-trained Language Models. [
qnn
] - [NeurIPS] Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning. [
qnn
] - [NeurIPS] ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers. [
qnn
] - [NeurIPS] ClimbQ: Class Imbalanced Quantization Enabling Robustness on Efficient Inferences. [
qnn
] - [IJCAI] FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer. [
qnn
] [code] [71⭐] - [ICLR] F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization. [
qnn
] - [ICLR] 8-bit Optimizers via Block-wise Quantization. [
qnn
] - [ICLR] Toward Efficient Low-Precision Training: Data Format Optimization and Hysteresis Quantization. [
qnn
] - [ICLR] Information Bottleneck: Exact Analysis of (Quantized) Neural Networks. [
qnn
] - [ICLR] QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization. [
qnn
] - [ICLR] SQuant: On-the-Fly Data-Free Quantization via Diagonal Hessian Approximation. [
qnn
][code] - [ICLR] Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks. [
snn
] - [ICLR] VC dimension of partially quantized neural networks in the overparametrized regime. [
qnn
] - [arxiv] Q-ViT: Fully Differentiable Quantization for Vision Transformer [
qnn
]
2021
- [ICLR] BiPointNet: Binary Neural Network for Point Clouds. [
bnn
] [torch] - [CVPR] Diversifying Sample Generation for Accurate Data-Free Quantization. [
qnn
] - [ACM MM] VQMG: Hierarchical Vector Quantised and Multi-hops Graph Reasoning for Explicit Representation Learning. [
other
] - [ACM MM] Fully Quantized Image Super-Resolution Networks. [
qnn
] - [NeurIPS] Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples. [
qnn
] - [NeurIPS] Post-Training Quantization for Vision Transformer. [
mixed
] - [NeurIPS] Post-Training Sparsity-Aware Quantization. [
qnn
] - [NeurIPS] Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals.
- [NeurIPS] VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization. [
other
] - [NeurIPS] Qu-ANTI-zation: Exploiting Quantization Artifacts for Achieving Adversarial Outcomes .
- [NeurIPS] A Winning Hand: Compressing Deep Networks Can Improve Out-of-Distribution Robustness. [
bnn
] [torch] - [CVPR] Learnable Companding Quantization for Accurate Low-bit Neural Networks. [
qnn
] - [CVPR] Zero-shot Adversarial Quantization. [
qnn
] [torch] - [CVPR] Binary Graph Neural Networks. [
bnn
] [torch] - [CVPR] Network Quantization with Element-wise Gradient Scaling. [
qnn
] [torch] - [CVPR] PokeBNN: A Binary Pursuit of Lightweight Accuracy [
bnn
] [tf] - [ICLR] BiPointNet: Binary Neural Network for Point Clouds. [
bnn
] [torch] - [ICLR] Reducing the Computational Cost of Deep Generative Models with Binary Neural Networks. [
bnn
] - [ICLR] High-Capacity Expert Binary Networks. [
bnn
] - [ICLR] Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Network. [
bnn
] - [ICLR] BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction. [
qnn
] [torch] - [ICLR] Neural gradients are near-lognormal: improved quantized and sparse training. [
qnn
] - [ICLR] Training with Quantization Noise for Extreme Model Compression. [
qnn
] - [ICLR] Incremental few-shot learning via vector quantization in deep embedded space. [
qnn
] - [ICLR] Degree-Quant: Quantization-Aware Training for Graph Neural Networks. [
qnn
] - [ICLR] BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization. [
qnn
] - [ICLR] Simple Augmentation Goes a Long Way: ADRL for DNN Quantization. [
qnn
] - [ICLR] Sparse Quantized Spectral Clustering. [
qnn
] - [ICLR] WrapNet: Neural Net Inference with Ultra-Low-Resolution Arithmetic. [
qnn
] - [ECCV] PAMS: Quantized Super-Resolution via Parameterized Max Scale. [
qnn
] - [AAAI] Distribution Adaptive INT8 Quantization for Training CNNs. [
qnn
] - [AAAI] Stochastic Precision Ensemble: Self‐Knowledge Distillation for Quantized Deep Neural Networks. [
qnn
] - [AAAI] Optimizing Information Theory Based Bitwise Bottlenecks for Efficient Mixed-Precision Activation Quantization. [
qnn
] - [AAAI] OPQ: Compressing Deep Neural Networks with One-shot Pruning-Quantization. [
qnn
] - [AAAI] Scalable Verification of Quantized Neural Networks. [
qnn
] - [AAAI] Uncertainty Quantification in CNN through the Bootstrap of Convex Neural Networks. [
qnn
] - [AAAI] FracBits: Mixed Precision Quantization via Fractional Bit-Widths. [
qnn
] - [AAAI] Post-‐training Quantization with Multiple Points: Mixed Precision without Mixed Precision. [
qnn
] - [AAAI] Vector Quantized Bayesian Neural Network Inference for Data Streams. [
qnn
] - [AAAI] TRQ: Ternary Neural Networks with Residual Quantization. [
qnn
] - [AAAI] Memory and Computation-Efficient Kernel SVM via Binary Embedding and Ternary Coefficients. [
bnn
] - [AAAI] Compressing Deep Convolutional Neural Networks by Stacking Low-Dimensional Binary Convolution Filters. [
bnn
] - [AAAI] Training Binary Neural Network without Batch Normalization for Image Super-Resolution. [
bnn
] - [AAAI] SA-BNN: State-Aware Binary Neural Network. [
bnn
] - [ACL] On the Distribution, Sparsity, and Inference-time Quantization of Attention Values in Transformers. [
qnn
] - [arxiv] Any-Precision Deep Neural Networks. [
mixed
] [torch] - [arxiv] ReCU: Reviving the Dead Weights in Binary Neural Networks. [
bnn
] [torch] - [arxiv] Post-Training Quantization for Vision Transformer. [
qnn
] - [arxiv] A Survey of Quantization Methods for Efficient Neural Network Inference.
- [arxiv] PTQ4ViT: Post-Training Quantization Framework for Vision Transformers. [
qnn
]
2020
- [CVPR] Forward and Backward Information Retention for Accurate Binary Neural Networks. [
bnn
] [torch] [105⭐] - [ACL] End to End Binarized Neural Networks for Text Classification. [
bnn
] - [AAAI] HLHLp: Quantized Neural Networks Traing for Reaching Flat Minima in Loss Sufrface. [
qnn
] - [AAAI] [72🔥] Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT. [
qnn
] - [AAAI] Sparsity-Inducing Binarized Neural Networks. [
bnn
] - [AAAI] Towards Accurate Low Bit-Width Quantization with Multiple Phase Adaptations.
- [COOL CHIPS] A Novel In-DRAM Accelerator Architecture for Binary Neural Network. [
hardware
] - [CoRR] Training Binary Neural Networks using the Bayesian Learning Rule. [
bnn
] - [CVPR] [47🔥] GhostNet: More Features from Cheap Operations. [
qnn
] [tensorflow & torch] [1.2k⭐] - [CVPR] APQ: Joint Search for Network Architecture, Pruning and Quantization Policy. [
qnn
] [torch] [76⭐] - [CVPR] Rotation Consistent Margin Loss for Efficient Low-Bit Face Recognition. [
qnn
] - [CVPR] BiDet: An Efficient Binarized Object Detector. [
qnn
] [torch] [112⭐] - [CVPR] Fixed-Point Back-Propagation Training. [video] [
qnn
] - [CVPR] Low-Bit Quantization Needs Good Distribution. [
qnn
] - [DATE] BNNsplit: Binarized Neural Networks for embedded distributed FPGA-based computing systems. [
bnn
] - [DATE] PhoneBit: Efficient GPU-Accelerated Binary Neural Network Inference Engine for Mobile Phones. [
bnn
] [hardware
] - [DATE] OrthrusPE: Runtime Reconfigurable Processing Elements for Binary Neural Networks. [
bnn
] - [ECCV] Learning Architectures for Binary Networks. [
bnn
] [torch] - [ECCV]PROFIT: A Novel Training Method for sub-4-bit MobileNet Models. [
qnn
] - [ECCV] ProxyBNN: Learning Binarized Neural Networks via Proxy Matrices. [
bnn
] - [ECCV] ReActNet: Towards Precise Binary Neural Network with Generalized Activation Functions. [
bnn
] [torch] [108⭐] - [ECCV] Differentiable Joint Pruning and Quantization for Hardware Efficiency. [
hardware
] - [EMNLP] TernaryBERT: Distillation-aware Ultra-low Bit BERT. [
qnn
] - [EMNLP] Fully Quantized Transformer for Machine Translation. [
qnn
] - [ICET] An Energy-Efficient Bagged Binary Neural Network Accelerator. [
bnn
] [hardware
] - [ICASSP] Balanced Binary Neural Networks with Gated Residual. [
bnn
] - [ICML] Training Binary Neural Networks through Learning with Noisy Supervision. [
bnn
] - [ICLR] DMS: Differentiable Dimension Search for Binary Neural Networks. [
bnn
] - [ICLR] [19🔥] Training Binary Neural Networks with Real-to-Binary Convolutions. [
bnn
] [code is comming] [re-implement] - [ICLR] BinaryDuo: Reducing Gradient Mismatch in Binary Activation Network by Coupling Binary Activations. [
bnn
] [torch] - [ICLR] Mixed Precision DNNs: All You Need is a Good Parametrization. [
mixed
] [code] [73⭐] - [IJCV] Binarized Neural Architecture Search for Efficient Object Recognition. [
bnn
] - [IJCAI] CP-NAS: Child-Parent Neural Architecture Search for Binary Neural Networks. [
bnn
] - [IJCAI] Towards Fully 8-bit Integer Inference for the Transformer Model. [
qnn
] [nlp
] - [IJCAI] Soft Threshold Ternary Networks. [
qnn
] - [IJCAI] Overflow Aware Quantization: Accelerating Neural Network Inference by Low-bit Multiply-Accumulate Operations. [
qnn
] - [IJCAI] Direct Quantization for Training Highly Accurate Low Bit-width Deep Neural Networks. [
qnn
] - [IJCAI] Fully Nested Neural Network for Adaptive Compression and Quantization. [
qnn
] - [ISCAS] MuBiNN: Multi-Level Binarized Recurrent Neural Network for EEG Signal Classification. [
bnn
] - [ISQED] BNN Pruning: Pruning Binary Neural Network Guided by Weight Flipping Frequency. [
bnn
] [torch] - [MICRO] GOBO: Quantizing Attention-Based NLP Models for Low Latency and Energy Efficient Inference. [
qnn
] [nlp
] - [MLST] Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML. [
hardware
] [qnn
] - [NeurIPS] Rotated Binary Neural Network. [
bnn
] [torch] - [NeurIPS] Searching for Low-Bit Weights in Quantized Neural Networks. [
qnn
] [torch] - [NeurIPS] Universally Quantized Neural Compression. [
qnn
] - [NeurIPS] Efficient Exact Verification of Binarized Neural Networks. [
bnn
] [torch] - [NeurIPS] Path Sample-Analytic Gradient Estimators for Stochastic Binary Networks. [
bnn
] [code] - [NeurIPS] HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks. [
qnn
] - [NeurIPS] Bayesian Bits: Unifying Quantization and Pruning. [
qnn
] - [NeurIPS] Robust Quantization: One Model to Rule Them All. [
qnn
] - [NeurIPS] Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow. [
qnn
] [torch] - [NeurIPS] Adaptive Gradient Quantization for Data-Parallel SGD. [
qnn
] [torch] - [NeurIPS] FleXOR: Trainable Fractional Quantization. [
qnn
] - [NeurIPS] Position-based Scaled Gradient for Model Quantization and Pruning. [
qnn
] [torch] - [NN] Training high-performance and large-scale deep neural networks with full 8-bit integers. [
qnn
] - [Neurocomputing] Eye localization based on weight binarization cascade convolution neural network. [
bnn
] - [PR] [23🔥] Binary neural networks: A survey. [
bnn
] - [PR Letters] Controlling information capacity of binary neural network. [
bnn
] - [SysML] Riptide: Fast End-to-End Binarized Neural Networks. [
qnn
] [tensorflow] [129⭐] - [TPAMI] Hierarchical Binary CNNs for Landmark Localization with Limited Resources. [
bnn
] [homepage] [code] - [TPAMI] Deep Neural Network Compression by In-Parallel Pruning-Quantization.
- [TPAMI] Towards Efficient U-Nets: A Coupled and Quantized Approach.
- [TVLSI] Phoenix: A Low-Precision Floating-Point Quantization Oriented Architecture for Convolutional Neural Networks. [
qnn
] - [WACV] MoBiNet: A Mobile Binary Network for Image Classification. [
bnn
] - [IEEE Access] An Energy-Efficient and High Throughput in-Memory Computing Bit-Cell With Excellent Robustness Under Process Variations for Binary Neural Network. [
bnn
] [hardware
] - [IEEE Trans. Magn] SIMBA: A Skyrmionic In-Memory Binary Neural Network Accelerator. [
bnn
] - [IEEE TCS.II] A Resource-Efficient Inference Accelerator for Binary Convolutional Neural Networks. [
hardware
] - [IEEE TCS.I] IMAC: In-Memory Multi-Bit Multiplication and ACcumulation in 6T SRAM Array. [
qnn
] - [IEEE Trans. Electron Devices] Design of High Robustness BNN Inference Accelerator Based on Binary Memristors. [
bnn
] [hardware
] - [arxiv] Training with Quantization Noise for Extreme Model Compression. [
qnn
] [torch] - [arxiv] Binarized Graph Neural Network. [
bnn
] - [arxiv] How Does Batch Normalization Help Binary Training? [
bnn
] - [arxiv] Distillation Guided Residual Learning for Binary Convolutional Neural Networks. [
bnn
] - [arxiv] Accelerating Binarized Neural Networks via Bit-Tensor-Cores in Turing GPUs. [
bnn
] [code] - [arxiv] MeliusNet: Can Binary Neural Networks Achieve MobileNet-level Accuracy? [
bnn
] [code] [192⭐] - [arxiv] RPR: Random Partition Relaxation for Training; Binary and Ternary Weight Neural Networks. [
bnn
] [qnn
] - [paper] Towards Lossless Binary Convolutional Neural Networks Using Piecewise Approximation. [
bnn
] - [arxiv] Understanding Learning Dynamics of Binary Neural Networks via Information Bottleneck. [
bnn
] - [arxiv] BinaryBERT: Pushing the Limit of BERT Quantization. [
bnn
] [nlp
] - [ECCV] BATS: Binary ArchitecTure Search. [
bnn
]
2019
- [AAAI] Efficient Quantization for Neural Networks with Binary Weights and Low Bitwidth Activations. [
qnn
] - [AAAI] [31🔥] Projection Convolutional Neural Networks for 1-bit CNNs via Discrete Back Propagation. [
bnn
] - [APCCAS] Using Neuroevolved Binary Neural Networks to solve reinforcement learning environments. [
bnn
] [code] - [BMVC] [32🔥] XNOR-Net++: Improved Binary Neural Networks. [
bnn
] - [BMVC] Accurate and Compact Convolutional Neural Networks with Trained Binarization. [
bnn
] - [CoRR] RBCN: Rectified Binary Convolutional Networks for Enhancing the Performance of 1-bit DCNNs. [
bnn
] - [CoRR] TentacleNet: A Pseudo-Ensemble Template for Accurate Binary Convolutional Neural Networks. [
bnn
] - [CoRR] Improved training of binary networks for human pose estimation and image recognition. [
bnn
] - [CoRR] Binarized Neural Architecture Search. [
bnn
] - [CoRR] Matrix and tensor decompositions for training binary neural networks. [
bnn
] - [CoRR] Back to Simplicity: How to Train Accurate BNNs from Scratch? [
bnn
] [code] [193⭐] - [CVPR] [53🔥] Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation. [
bnn
] - [CVPR] SeerNet: Predicting Convolutional Neural Network Feature-Map Sparsity Through Low-Bit Quantization. [
qnn
] - [CVPR] [218🔥] HAQ: Hardware-Aware Automated Quantization with Mixed Precision. [
qnn
] [hardware
] [torch] [233⭐] - [CVPR] [48🔥] Quantization Networks. [
bnn
] [torch] [82⭐] - [CVPR] Fully Quantized Network for Object Detection. [
qnn
] - [CVPR] Learning Channel-Wise Interactions for Binary Convolutional Neural Networks. [
bnn
] - [CVPR] [31🔥] Circulant Binary Convolutional Networks: Enhancing the Performance of 1-bit DCNNs with Circulant Back Propagation. [
bnn
] - [CVPR] [36🔥] Regularizing Activation Distribution for Training Binarized Deep Networks. [
bnn
] - [CVPR] A Main/Subsidiary Network Framework for Simplifying Binary Neural Network. [
bnn
] - [CVPR] Binary Ensemble Neural Network: More Bits per Network or More Networks per Bit? [
bnn
] - [FPGA] Towards Fast and Energy-Efficient Binarized Neural Network Inference on FPGA. [
bnn
] [hardware
] - [GLSVLSI] Binarized Depthwise Separable Neural Network for Object Tracking in FPGA. [
bnn
] [hardware
] - [ICCV] [55🔥] Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks. [
qnn
] - [ICCV] Bayesian optimized 1-bit cnns. [
bnn
] - [ICCV] Searching for Accurate Binary Neural Architectures. [
bnn
] - [ICCV] Data-Free Quantization Through Weight Equalization and Bias Correction. [
qnn
] [hardware
] [torch] - [ICML] Efficient 8-Bit Quantization of Transformer Neural Machine Language Translation Model. [
qnn
] [nlp
] - [ICLR] [37🔥] ProxQuant: Quantized Neural Networks via Proximal Operators. [
qnn
] [torch] - [ICLR] An Empirical study of Binary Neural Networks' Optimisation. [
bnn
] - [ICIP] Training Accurate Binary Neural Networks from Scratch. [
bnn
] [code] [192⭐] - [ICUS] Balanced Circulant Binary Convolutional Networks. [
bnn
] - [IJCAI] Binarized Neural Networks for Resource-Efficient Hashing with Minimizing Quantization Loss. [
bnn
] - [IJCAI] Binarized Collaborative Filtering with Distilling Graph Convolutional Network. [
bnn
] - [ISOCC] Dual Path Binary Neural Network. [
bnn
] - [IEEE J. Emerg. Sel. Topics Circuits Syst.] Hyperdrive: A Multi-Chip Systolically Scalable Binary-Weight CNN Inference Engine. [
hardware
] - [IEEE JETC] [128🔥] Eyeriss v2: A Flexible Accelerator for Emerging Deep Neural Networks on Mobile Devices. [
hardware
] - [IEEE J. Solid-State Circuits] An Energy-Efficient Reconfigurable Processor for Binary-and Ternary-Weight Neural Networks With Flexible Data Bit Width. [
qnn
] - [MDPI Electronics] A Review of Binarized Neural Networks. [
bnn
] - [NeurIPS] MetaQuant: Learning to Quantize by Learning to Penetrate Non-differentiable Quantization. [
qnn
] [torch] - [NeurIPS] Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization. [
bnn
] [tensorflow] - [NeurIPS] [43🔥] Regularized Binary Network Training. [
bnn
] - [NeurIPS] [44🔥] Q8BERT: Quantized 8Bit BERT. [
qnn
] [nlp
] - [NeurIPS] Fully Quantized Transformer for Improved Translation. [
qnn
] [nlp
] - [NeurIPS] Normalization Helps Training of Quantized LSTM. [
qnn
] [bnn
] - [RoEduNet] PXNOR: Perturbative Binary Neural Network. [
bnn
] [code] - [SiPS] Knowledge distillation for optimization of quantized deep neural networks. [
qnn
] - [TMM] [45🔥] Deep Binary Reconstruction for Cross-Modal Hashing. [
bnn
] - [TMM] Compact Hash Code Learning With Binary Deep Neural Network. [
bnn
] - [IEEE TCS.I] Xcel-RAM: Accelerating Binary Neural Networks in High-Throughput SRAM Compute Arrays. [
hardware
] - [IEEE TCS.I] Recursive Binary Neural Network Training Model for Efficient Usage of On-Chip Memory. [
bnn
] - [VLSI-SoC] A Product Engine for Energy-Efficient Execution of Binary Neural Networks Using Resistive Memories. [
bnn
] [hardware
] - [paper] [43🔥] BNN+: Improved Binary Network Training. [
bnn
] - [arxiv] Self-Binarizing Networks. [
bnn
] - [arxiv] Towards Unified INT8 Training for Convolutional Neural Network. [
qnn
] - [arxiv] daBNN: A Super Fast Inference Framework for Binary Neural Networks on ARM devices. [
bnn
] [hardware
] [code] - [arxiv] QKD: Quantization-aware Knowledge Distillation. [
qnn
] - [arxiv] [59🔥] Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search. [
qnn
]
2018
- [AAAI] From Hashing to CNNs: Training BinaryWeight Networks via Hashing. [
bnn
] - [AAAI] [136🔥] Extremely Low Bit Neural Network: Squeeze the Last Bit Out with ADMM. [
qnn
] [homepage] - [CAAI] Fast object detection based on binary deep convolution neural networks. [
bnn
] - [CoRR] LightNN: Filling the Gap between Conventional Deep Neural Networks and Binarized Networks. [
bnn
] - [CoRR] BinaryRelax: A Relaxation Approach For Training Deep Neural Networks With Quantized Weights. [
bnn
] - [CVPR] [63🔥] Two-Step Quantization for Low-bit Neural Networks. [
qnn
] - [CVPR] Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations. [
qnn
] - [CVPR] [97🔥] Towards Effective Low-bitwidth Convolutional Neural Networks. [
qnn
] - [CVPR] Modulated convolutional networks. [
bnn
] - [CVPR] [67🔥] SYQ: Learning Symmetric Quantization For Efficient Deep Neural Networks. [
qnn
] [code] - [CVPR] [630🔥] Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference. [
qnn
] - [ECCV] Training Binary Weight Networks via Semi-Binary Decomposition. [
bnn
] - [ECCV] [47🔥] TBN: Convolutional Neural Network with Ternary Inputs and Binary Weights. [
bnn
] [qnn
] [torch] - [ECCV] [202🔥] LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks. [
qnn
] [tensorflow] [188⭐] - [ECCV] [145🔥] Bi-Real Net: Enhancing the Performance of 1-bit CNNs With Improved Representational Capability and Advanced Training Algorithm. [
bnn
] [torch] [120⭐] - [FCCM] ReBNet: Residual Binarized Neural Network. [
bnn
] [tensorflow] - [FPL] FBNA: A Fully Binarized Neural Network Accelerator. [
hardware
] - [ICLR] [65🔥] Loss-aware Weight Quantization of Deep Networks. [
qnn
] [code] - [ICLR] [230🔥] Model compression via distillation and quantization. [
qnn
] [torch] [284⭐] - [ICLR] [201🔥] PACT: Parameterized Clipping Activation for Quantized Neural Networks. [
qnn
] - [ICLR] [168🔥] WRPN: Wide Reduced-Precision Networks. [
qnn
] - [ICLR] Analysis of Quantized Models. [
qnn
] - [ICLR] [141🔥] Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy. [
qnn
] - [IJCAI] Deterministic Binary Filters for Convolutional Neural Networks. [
bnn
] - [IJCAI] Planning in Factored State and Action Spaces with Learned Binarized Neural Network Transition Models. [
bnn
] - [IJCNN] Analysis and Implementation of Simple Dynamic Binary Neural Networks. [
bnn
] - [IPDPS] BitFlow: Exploiting Vector Parallelism for Binary Neural Networks on CPU. [
bnn
] - [IEEE J. Solid-State Circuits] [66🔥] BRein Memory: A Single-Chip Binary/Ternary Reconfigurable in-Memory Deep Neural Network Accelerator Achieving 1.4 TOPS at 0.6 W. [
hardware
] [qnn
] - [NCA] [88🔥] A survey of FPGA-based accelerators for convolutional neural networks. [
hardware
] - [NeurIPS] [150🔥] Training Deep Neural Networks with 8-bit Floating Point Numbers. [
qnn
] - [NeurIPS] [91🔥] Scalable methods for 8-bit training of neural networks. [
qnn
] [torch] - [MM] BitStream: Efficient Computing Architecture for Real-Time Low-Power Inference of Binary Neural Networks on CPUs. [
bnn
] - [Res Math Sci] Blended coarse gradient descent for full quantization of deep neural networks. [
qnn
] [bnn
] - [TCAD] XNOR Neural Engine: A Hardware Accelerator IP for 21.6-fJ/op Binary Neural Network Inference. [
hardware
] - [TRETS] [50🔥] FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks. [
qnn
] - [TVLSI] An Energy-Efficient Architecture for Binary Weight Convolutional Neural Networks. [
bnn
] - [arxiv] Training Competitive Binary Neural Networks from Scratch. [
bnn
] [code] [192⭐] - [arxiv] Joint Neural Architecture Search and Quantization. [
qnn
] [torch] - [CVPR] Explicit loss-error-aware quantization for low-bit deep neural networks. [
qnn
]
2017
- [CoRR] BMXNet: An Open-Source Binary Neural Network Implementation Based on MXNet. [
bnn
] [code] - [CVPR] [251🔥] Deep Learning with Low Precision by Half-wave Gaussian Quantization. [
qnn
] [code] [118⭐] - [CVPR] [156🔥] Local Binary Convolutional Neural Networks. [
bnn
] [torch] [94⭐] - [FPGA] [463🔥] FINN: A Framework for Fast, Scalable Binarized Neural Network Inference. [
hardware
] [bnn
] - [ICASSP)] Fixed-point optimization of deep neural networks with adaptive step size retraining. [
qnn
] - [ICCV] [130🔥] Binarized Convolutional Landmark Localizers for Human Pose Estimation and Face Alignment with Limited Resources. [
bnn
] [homepage] [torch] [207⭐] - [ICCV] [55🔥] Performance Guaranteed Network Acceleration via High-Order Residual Quantization. [
qnn
] - [ICLR] [554🔥] Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights. [
qnn
] [torch] [144⭐] - [ICLR] [119🔥] Loss-aware Binarization of Deep Networks. [
bnn
] [code] - [ICLR] [222🔥] Soft Weight-Sharing for Neural Network Compression. [
other
] - [ICLR] [637🔥] Trained Ternary Quantization. [
qnn
] [torch] [90⭐] - [InterSpeech] Binary Deep Neural Networks for Speech Recognition. [
bnn
] - [IPDPSW] On-Chip Memory Based Binarized Convolutional Deep Neural Network Applying Batch Normalization Free Technique on an FPGA. [
hardware
] - [JETC] A GPU-Outperforming FPGA Accelerator Architecture for Binary Convolutional Neural Networks. [
hardware
] [bnn
] - [NeurIPS] [293🔥] Towards Accurate Binary Convolutional Neural Network. [
bnn
] [tensorflow] - [Neurocomputing] [126🔥] FP-BNN: Binarized neural network on FPGA. [
hardware
] - [MWSCAS] Deep learning binary neural network on an FPGA. [
hardware
] [bnn
] - [arxiv] [71🔥] Ternary Neural Networks with Fine-Grained Quantization. [
qnn
] - [arxiv] ShiftCNN: Generalized Low-Precision Architecture for Inference of Convolutional Neural Networks. [
qnn
] [code] [53⭐]
2016
- [CoRR] [1k🔥] DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients. [
qnn
] [code] [5.8k⭐] - [ECCV] [2.7k🔥] XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks. [
bnn
] [torch] [787⭐] - [ICASSP)] Fixed-point Performance Analysis of Recurrent Neural Networks. [
qnn
] - [NeurIPS] [572🔥] Ternary weight networks. [
qnn
] [code] [61⭐] - [NeurIPS)] [1.7k🔥] Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1. [
bnn
] [torch] [239⭐] - [CVPR] [270🔥] Quantized convolutional neural networks for mobile devices. code
2015
- [ICML] [191🔥] Bitwise Neural Networks. [
bnn
] - [NeurIPS] [1.8k🔥] BinaryConnect: Training Deep Neural Networks with binary weights during propagations. [
bnn
] [code] [330⭐] - [arxiv] Resiliency of Deep Neural Networks under quantizations. [
qnn
]
Codes_and_Docs
-
[Doc] ZF-Net: An Open Source FPGA CNN Library.
-
[Doc] Accelerating CNN inference on FPGAs: A Survey.
-
[中文] An Overview of Deep Compression Approaches.
-
[中文] 嵌入式深度学习之神经网络二值化 - FPGA实现
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[综述] 二值神经网络 Binary Neural Networks
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