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 4401–4425 of 4925 papers

TitleStatusHype
EfQAT: An Efficient Framework for Quantization-Aware Training—0
Ef-QuantFace: Streamlined Face Recognition with Small Data and Low-Bit Precision—0
Elastic Significant Bit Quantization and Acceleration for Deep Neural Networks—0
ELMGS: Enhancing memory and computation scaLability through coMpression for 3D Gaussian Splatting—0
Embedded Phase Shifting: Robust Phase Shifting With Embedded Signals—0
Embedding Compression for Efficient Re-Identification—0
Embedding Compression with Isotropic Iterative Quantization—0
Emergent Quantized Communication—0
Emotion Recognition Using Speaker Cues—0
Empirical Evaluation of Post-Training Quantization Methods for Language Tasks—0
Emulation Learning for Neuromimetic Systems—0
Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications—0
Fast and Efficient 2-bit LLM Inference on GPU: 2/4/16-bit in a Weight Matrix with Asynchronous Dequantization—0
Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment—0
Enabling On-Device CNN Training by Self-Supervised Instance Filtering and Error Map Pruning—0
Enabling On-device Continual Learning with Binary Neural Networks—0
Enabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptation—0
Enabling Fast Deep Learning on Tiny Energy-Harvesting IoT Devices—0
Encoder-Quantization-Motion-based Video Quality Metrics—0
End-to-End Autoencoder Communications with Optimized Interference Suppression—0
End-to-end Binary Representation Learning via Direct Binary Embedding—0
End-to-end codesign of Hessian-aware quantized neural networks for FPGAs and ASICs—0
End-to-End Efficient Representation Learning via Cascading Combinatorial Optimization—0
End-to-end fully-binarized network design: from Generic Learned Thermometer to Block Pruning—0
End-to-end Keyword Spotting using Neural Architecture Search and 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