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 4201–4250 of 4925 papers

TitleStatusHype
Device Interoperability for Learned Image Compression with Weights and Activations Quantization—0
DFTerNet: Towards 2-bit Dynamic Fusion Networks for Accurate Human Activity Recognition—0
Diagnostic data integration using deep neural networks for real-time plasma analysis—0
Differentiable Discrete Device-to-System Codesign for Optical Neural Networks via Gumbel-Softmax—0
Differentiable Dynamic Quantization with Mixed Precision and Adaptive Resolution—0
Differentiable Joint Pruning and Quantization for Hardware Efficiency—0
Differentiable Product Quantization for Learning Compact Embedding Layers—0
Differentiable Search for Finding Optimal Quantization Strategy—0
Differentiable Training for Hardware Efficient LightNNs—0
Differential Deep Detection in Massive MIMO With One-Bit ADC—0
Differential error feedback for communication-efficient decentralized learning—0
Differential Modulation in Massive MIMO With Low-Resolution ADCs—0
Differential Privacy with Random Projections and Sign Random Projections—0
Diffusion-based Perceptual Neural Video Compression with Temporal Diffusion Information Reuse—0
Diffusion Product Quantization—0
DilateQuant: Accurate and Efficient Diffusion Quantization via Weight Dilation—0
DILEMMA: Joint LLM Quantization and Distributed LLM Inference Over Edge Computing Systems—0
Dimensionality-Dependent Generalization Bounds for k-Dimensional Coding Schemes—0
Dimension-Free Bounds for Low-Precision Training—0
DipSVD: Dual-importance Protected SVD for Efficient LLM Compression—0
Direct Quantization for Training Highly Accurate Low Bit-width Deep Neural Networks—0
DI-Retinex: Digital-Imaging Retinex Theory for Low-Light Image Enhancement—0
Dirichlet Mixture Model based VQ Performance Prediction for Line Spectral Frequency—0
Disco Intelligent Omni-Surfaces: 360-degree Fully-Passive Jamming Attacks—0
Disco Intelligent Reflecting Surfaces: Active Channel Aging for Fully-Passive Jamming Attacks—0
DisCoRD: Discrete Tokens to Continuous Motion via Rectified Flow Decoding—0
Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Embedded Inference—0
Discovering Patterns in Time-Varying Graphs: A Triclustering Approach—0
Discrete Audio Representation as an Alternative to Mel-Spectrograms for Speaker and Speech Recognition—0
Discrete Contrastive Learning for Diffusion Policies in Autonomous Driving—0
Discrete-Valued Neural Communication—0
Discrete-Valued Neural Networks Using Variational Inference—0
Discriminative Cross-View Binary Representation Learning—0
Disentangled Representation Learning for Unsupervised Neural Quantization—0
Disentangling segmental and prosodic factors to non-native speech comprehensibility—0
DiskANN++: Efficient Page-based Search over Isomorphic Mapped Graph Index using Query-sensitivity Entry Vertex—0
Dissecting the Runtime Performance of the Training, Fine-tuning, and Inference of Large Language Models—0
Distance-aware Quantization—0
Distance Encoded Product Quantization—0
Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics—0
Distilled Low Rank Neural Radiance Field with Quantization for Light Field Compression—0
Distilling Vision-Language Pretraining for Efficient Cross-Modal Retrieval—0
Distinctive Feature Codec: Adaptive Segmentation for Efficient Speech Representation—0
Distinguished Quantized Guidance for Diffusion-based Sequence Recommendation—0
Distinguishing Posed and Spontaneous Smiles by Facial Dynamics—0
Distortion-Controlled Dithering with Reduced Recompression Rate—0
Distributed Average Consensus under Quantized Communication via Event-Triggered Mass Summation—0
Distributed Average Consensus under Quantized Communication via Event-Triggered Mass Splitting—0
Distributed Chernoff Test: Optimal decision systems over networks—0
Distributed Computation of Exact Average Degree and Network Size in Finite Number of Steps under Quantized Communication—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