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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2776–2800 of 4925 papers

TitleStatusHype
Detecting Face Synthesis Using a Concealed Fusion Model—0
DHNet: Double MPEG-4 Compression Detection via Multiple DCT Histograms—0
Detection of small changes in medical and random-dot images comparing self-organizing map performance to human detection—0
Development of a Thermodynamics of Human Cognition and Human Culture—0
Development of Quantized DNN Library for Exact Hardware Emulation—0
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
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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