SOTAVerified

Quantization

Quantization is a promising technique to reduce the computation cost of neural network training, which can replace high-cost floating-point numbers (e.g., float32) with low-cost fixed-point numbers (e.g., int8/int16).

Source: Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers

Papers

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Showing 3651–3700 of 4925 papers

TitleStatusHype
FEDZIP: A Compression Framework for Communication-Efficient Federated LearningCode0
Image Splicing Detection, Localization and Attribution via JPEG Primary Quantization Matrix Estimation and Clustering—0
Probabilistic Learning Vector Quantization on Manifold of Symmetric Positive Definite Matrices—0
Rescuing Deep Hashing from Dead Bits Problem—0
Understanding Cache Boundness of ML Operators on ARM ProcessorsCode0
CAMBI: Contrast-aware Multiscale Banding Index—0
Performance of Cell-Free MmWave Massive MIMO Systems with Fronthaul Compression and DAC Quantization—0
AdderNet and its Minimalist Hardware Design for Energy-Efficient Artificial Intelligence—0
Pruning and Quantization for Deep Neural Network Acceleration: A Survey—0
Error Diffusion Halftoning Against Adversarial ExamplesCode0
Continual Learning of Generative Models with Limited Data: From Wasserstein-1 Barycenter to Adaptive Coalescence—0
Overfitting for Fun and Profit: Instance-Adaptive Data Compression—0
Generative Zero-shot Network Quantization—0
Time-Correlated Sparsification for Communication-Efficient Federated Learning—0
ES-ENAS: Efficient Evolutionary Optimization for Large Hybrid Search SpacesCode0
Multi-Task Network Pruning and Embedded Optimization for Real-time Deployment in ADAS—0
Deep Compression of Neural Networks for Fault Detection on Tennessee Eastman Chemical Processes—0
KDLSQ-BERT: A Quantized Bert Combining Knowledge Distillation with Learned Step Size Quantization—0
On the quantization of recurrent neural networks—0
Towards Energy Efficient Federated Learning over 5G+ Mobile Devices—0
Single-path Bit Sharing for Automatic Loss-aware Model Compression—0
Energy-Efficient Distributed Learning Algorithms for Coarsely Quantized Signals—0
Activation Density based Mixed-Precision Quantization for Energy Efficient Neural Networks—0
Sound Event Detection with Binary Neural Networks on Tightly Power-Constrained IoT Devices—0
Computational data analysis for first quantization estimation on JPEG double compressed imagesCode0
Quantization optimized with respect to the Haar basis—0
Who's a Good Boy? Reinforcing Canine Behavior in Real-Time using Machine LearningCode0
Noise Sensitivity-Based Energy Efficient and Robust Adversary Detection in Neural Networks—0
End-to-end Quantized Training via Log-Barrier Extensions—0
Product Quantizer Aware Inverted Index for Scalable Nearest Neighbor Search—0
Practical Locally Private Federated Learning with Communication Efficiency—0
Uniform-Precision Neural Network Quantization via Neural Channel Expansion—0
Incremental few-shot learning via vector quantization in deep embedded space—0
Improving Low-Precision Network Quantization via Bin Regularization—0
TwinDNN: A Tale of Two Deep Neural Networks—0
Explore the Potential of CNN Low Bit Training—0
DQSGD: DYNAMIC QUANTIZED STOCHASTIC GRADIENT DESCENT FOR COMMUNICATION-EFFICIENT DISTRIBUTED LEARNING—0
Simple Augmentation Goes a Long Way: ADRL for DNN Quantization—0
Improving the accuracy of neural networks in analog computing-in-memory systems by a generalized quantization method—0
Post-Training Weighted Quantization of Neural Networks for Language Models—0
Collaborative Filtering with Smooth Reconstruction of the Preference Function—0
Semi-Relaxed Quantization with DropBits: Training Low-Bit Neural Networks via Bitwise Regularization—0
WrapNet: Neural Net Inference with Ultra-Low-Precision Arithmetic—0
WAVEQ: GRADIENT-BASED DEEP QUANTIZATION OF NEURAL NETWORKS THROUGH SINUSOIDAL REGULARIZATIONCode0
Hybrid and Non-Uniform DNN quantization methods using Retro Synthesis data for efficient inference—0
Multi-Prize Lottery Ticket Hypothesis: Finding Generalizable and Efficient Binary Subnetworks in a Randomly Weighted Neural Network—0
Weights Having Stable Signs Are Important: Finding Primary Subnetworks and Kernels to Compress Binary Weight Networks—0
Learned Multi-Resolution Variable-Rate Image Compression with Octave-based Residual Blocks—0
BinaryBERT: Pushing the Limit of BERT Quantization—0
Improving Adversarial Robustness in Weight-quantized Neural Networks—0
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All datasetsImageNetCIFAR-10Wiki-40BAgeDB-30CFP-FPCOCO (Common Objects in Context)IJB-BIJB-CKnowledge-based:LFW

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FQ-ViT (ViT-L)Top-1 Accuracy (%)85.03—Unverified
2FQ-ViT (ViT-B)Top-1 Accuracy (%)83.31—Unverified
3FQ-ViT (Swin-B)Top-1 Accuracy (%)82.97—Unverified
4FQ-ViT (Swin-S)Top-1 Accuracy (%)82.71—Unverified
5FQ-ViT (DeiT-B)Top-1 Accuracy (%)81.2—Unverified
6FQ-ViT (Swin-T)Top-1 Accuracy (%)80.51—Unverified
7FQ-ViT (DeiT-S)Top-1 Accuracy (%)79.17—Unverified
8Xception W8A8Top-1 Accuracy (%)78.97—Unverified
9ADLIK-MO-ResNet50-W4A4Top-1 Accuracy (%)77.88—Unverified
10ADLIK-MO-ResNet50-W3A4Top-1 Accuracy (%)77.34—Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_3MAP160,327.04—Unverified
2DTQMAP0.79—Unverified
#ModelMetricClaimedVerifiedStatus
1OutEffHop-Bert_basePerplexity6.3—Unverified
2OutEffHop-Bert_basePerplexity6.21—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy98.13—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy92.92—Unverified
#ModelMetricClaimedVerifiedStatus
1SSD ResNet50 V1 FPN 640x640MAP34.3—Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-495.13—Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-496.38—Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_5All84,809,664—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy99.8—Unverified