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 4001–4025 of 4925 papers

TitleStatusHype
Kernel Quantization for Efficient Network Compression—0
Communication-efficient Variance-reduced Stochastic Gradient Descent—0
Distributed Deep Convolutional Compression for Massive MIMO CSI Feedback—0
Optimizing JPEG Quantization for Classification Networks—0
Cluster Pruning: An Efficient Filter Pruning Method for Edge AI Vision Applications—0
A Survey on Deep Hashing Methods—0
Phoenix: A Low-Precision Floating-Point Quantization Oriented Architecture for Convolutional Neural Networks—0
Image Hashing by Minimizing Discrete Component-wise Wasserstein DistanceCode0
WaveQ: Gradient-Based Deep Quantization of Neural Networks through Sinusoidal Adaptive Regularization—0
Quantized Neural Network Inference with Precision Batching—0
Moniqua: Modulo Quantized Communication in Decentralized SGD—0
Adversarial Attack on Deep Product Quantization Network for Image Retrieval—0
Optimal Gradient Quantization Condition for Communication-Efficient Distributed Training—0
Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees—0
Non-Volatile Memory Array Based Quantization- and Noise-Resilient LSTM Neural Networks—0
OptComNet: Optimized Neural Networks for Low-Complexity Channel Estimation—0
Revisiting Saliency Metrics: Farthest-Neighbor Area Under CurveCode0
PoET-BiN: Power Efficient Tiny Binary Neurons—0
Quantized Decentralized Stochastic Learning over Directed Graphs—0
New Bounds For Distributed Mean Estimation and Variance Reduction—0
Uncertainty Principle for Communication Compression in Distributed and Federated Learning and the Search for an Optimal Compressor—0
Post-training Quantization with Multiple Points: Mixed Precision without Mixed Precision—0
Learning Multi-granular Quantized Embeddings for Large-Vocab Categorical Features in Recommender Systems—0
SYMOG: learning symmetric mixture of Gaussian modes for improved fixed-point quantization—0
Gradient _1 Regularization for Quantization Robustness—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