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 826–850 of 4925 papers

TitleStatusHype
TinyLSTMs: Efficient Neural Speech Enhancement for Hearing AidsCode1
MicroNet for Efficient Language ModelingCode1
Bi3D: Stereo Depth Estimation via Binary ClassificationsCode1
Bayesian Bits: Unifying Quantization and PruningCode1
Data-Free Network Quantization With Adversarial Knowledge DistillationCode1
A Little Bit More: Bitplane-Wise Bit-Depth RecoveryCode1
Lite Transformer with Long-Short Range AttentionCode1
LSQ+: Improving low-bit quantization through learnable offsets and better initializationCode1
Technical Report: NEMO DNN Quantization for Deployment ModelCode1
Minimizing FLOPs to Learn Efficient Sparse RepresentationsCode1
LogicNets: Co-Designed Neural Networks and Circuits for Extreme-Throughput ApplicationsCode1
Feature Quantization Improves GAN TrainingCode1
Single-Image HDR Reconstruction by Learning to Reverse the Camera PipelineCode1
Learning to Structure an Image with Few ColorsCode1
DNN+NeuroSim V2.0: An End-to-End Benchmarking Framework for Compute-in-Memory Accelerators for On-chip TrainingCode1
Fast Distance-based Anomaly Detection in Images Using an Inception-like AutoencoderCode1
Generative Low-bitwidth Data Free QuantizationCode1
Ternary Compression for Communication-Efficient Federated LearningCode1
Probability Weighted Compact Feature for Domain Adaptive RetrievalCode1
VQ-DRAW: A Sequential Discrete VAECode1
Automatic Perturbation Analysis for Scalable Certified Robustness and BeyondCode1
Generalized Product Quantization Network for Semi-supervised Image RetrievalCode1
Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of TransformersCode1
Searching for Winograd-aware Quantized NetworksCode1
Exploring the Connection Between Binary and Spiking Neural NetworksCode1
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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