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 1551–1575 of 4925 papers

TitleStatusHype
End-to-End Optimized Speech Coding with Deep Neural Networks—0
End-to-end Quantized Training via Log-Barrier Extensions—0
End-to-End Rate-Distortion Optimization for Bi-Directional Learned Video Compression—0
An Implementation of Vector Quantization using the Genetic Algorithm Approach—0
EDAS: Efficient and Differentiable Architecture Search—0
Adaptive Asymmetric Label-guided Hashing for Multimedia Search—0
Energy-Aware LLMs: A step towards sustainable AI for downstream applications—0
Energy awareness in low precision neural networks—0
Energy Efficiency Maximization Precoding for Quantized Massive MIMO Systems—0
Energy Efficiency Optimization for Millimeter Wave System with Resolution-Adaptive ADCs—0
Energy-Efficient Distributed Learning Algorithms for Coarsely Quantized Signals—0
Energy-efficient Knowledge Distillation for Spiking Neural Networks—0
Energy Efficient Learning with Low Resolution Stochastic Domain Wall Synapse Based Deep Neural Networks—0
Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants—0
Energy-Efficient Transformer Inference: Optimization Strategies for Time Series Classification—0
Engineering the Neural Automatic Passenger Counter—0
ECG Biometric Authentication Using Self-Supervised Learning for IoT Edge Sensors—0
Enhanced Bayesian Compression via Deep Reinforcement Learning—0
BlueLM-V-3B: Algorithm and System Co-Design for Multimodal Large Language Models on Mobile Devices—0
Single-path Bit Sharing for Automatic Loss-aware Model Compression—0
Fast binary embeddings, and quantized compressed sensing with structured matrices—0
Enhancement Of Coded Speech Using a Mask-Based Post-Filter—0
Fast, Compact, and High Quality LSTM-RNN Based Statistical Parametric Speech Synthesizers for Mobile Devices—0
Block-Wise Dynamic-Precision Neural Network Training Acceleration via Online Quantization Sensitivity Analytics—0
Reclaiming Residual Knowledge: A Novel Paradigm to Low-Bit 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