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 3601–3625 of 4925 papers

TitleStatusHype
Set-Theoretic Learning for Detection in Cell-Less C-RAN Systems—0
Reduced Precision Strategies for Deep Learning: A High Energy Physics Generative Adversarial Network Use Case—0
Data-free mixed-precision quantization using novel sensitivity metric—0
Learned Gradient Compression for Distributed Deep Learning—0
Efficient Randomized Subspace Embeddings for Distributed Optimization under a Communication BudgetCode0
VMAF-based Bitrate Ladder Estimation for Adaptive Streaming—0
Learnable Companding Quantization for Accurate Low-bit Neural Networks—0
BDD4BNN: A BDD-based Quantitative Analysis Framework for Binarized Neural Networks—0
Quantization-Guided Training for Compact TinyML Models—0
MWQ: Multiscale Wavelet Quantized Neural Networks—0
Wav2vec-C: A Self-supervised Model for Speech Representation Learning—0
Reliability-Aware Quantization for Anti-Aging NPUs—0
Pufferfish: Communication-efficient Models At No Extra CostCode0
Neural Network-based Quantization for Network Automation—0
Deep Neural Network Models Compression—0
Effective and Fast: A Novel Sequential Single Path Search for Mixed-Precision Quantization—0
NETWORK COMPRESSION FOR MACHINE-LEARNT FLUID SIMULATIONS—0
Adaptive Transmission for Distributed Detection in Energy Harvesting Wireless Sensor Networks—0
SME: ReRAM-based Sparse-Multiplication-Engine to Squeeze-Out Bit Sparsity of Neural Network—0
Improved Techniques for Quantizing Deep Networks with Adaptive Bit-Widths—0
A Structurally Regularized Convolutional Neural Network for Image Classification using Wavelet-based SubBand Decomposition—0
Diversifying Sample Generation for Accurate Data-Free Quantization—0
SWIS -- Shared Weight bIt Sparsity for Efficient Neural Network Acceleration—0
Human Symmetry Uncertainty Detected by a Self-Organizing Neural Network Map—0
Improving Approximate Optimal Transport Distances using Quantization—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