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 2501–2525 of 4925 papers

TitleStatusHype
Quantized Proximal Averaging Network for Analysis Sparse Coding—0
Quantized Decentralized Stochastic Learning over Directed Graphs—0
Quantized Random Projections and Non-Linear Estimation of Cosine Similarity—0
Quantized Rank Reduction: A Communications-Efficient Federated Learning Scheme for Network-Critical Applications—0
Quantized sparse PCA for neural network weight compression—0
Quantized Sparse Weight Decomposition for Neural Network Compression—0
Quantized State Feedback Stabilization of Nonlinear Systems under Denial-of-Service—0
Quantized Three-Ion-Channel Neuron Model for Neural Action Potentials—0
Quantized Wasserstein Procrustes Alignment of Word Embedding Spaces—0
Quantized Zero Dynamics Attacks against Sampled-data Control Systems—0
Quantizing Convolutional Neural Networks for Low-Power High-Throughput Inference Engines—0
Quantizing data for distributed learning—0
Quantizing Diffusion Models from a Sampling-Aware Perspective—0
Quantizing Heavy-tailed Data in Statistical Estimation: (Near) Minimax Rates, Covariate Quantization, and Uniform Recovery—0
Quantizing Small-Scale State-Space Models for Edge AI—0
Quantizing YOLOv7: A Comprehensive Study—0
QuantNet: Learning to Quantize by Learning within Fully Differentiable Framework—0
QuantTune: Optimizing Model Quantization with Adaptive Outlier-Driven Fine Tuning—0
Quantum Neural Network Compression—0
Quantum-secure multiparty deep learning—0
Human Perception as a Phenomenon of Quantization—0
QuantuneV2: Compiler-Based Local Metric-Driven Mixed Precision Quantization for Practical Embedded AI Applications—0
QuantX: A Framework for Hardware-Aware Quantization of Generative AI Workloads—0
Quasar-ViT: Hardware-Oriented Quantization-Aware Architecture Search for Vision Transformers—0
QuATON: Quantization Aware Training of Optical Neurons—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