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 4426–4450 of 4925 papers

TitleStatusHype
End-to-End Latent Fingerprint Search—0
End-to-End Learned Image Compression with Quantized Weights and Activations—0
End-to-end Learning of Compressible Features—0
End-to-End Neural Network Compression via _1_2 Regularized Latency Surrogates—0
End-to-end optimization of nonlinear transform codes for perceptual quality—0
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
End-to-end workflow for machine learning-based qubit readout with QICK and hls4ml—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
English K_Quantization of LLMs Does Not Disproportionately Diminish Multilingual Performance—0
Enhanced Bayesian Compression via Deep Reinforcement Learning—0
Enhanced Blind Calibration of Uniform Linear Arrays with One-Bit Quantization by Kullback-Leibler Divergence Covariance Fitting—0
Enhance Feature Discrimination for Unsupervised Hashing—0
Enhancement Of Coded Speech Using a Mask-Based Post-Filter—0
Enhancing Bridge Deck Delamination Detection Based on Aerial Thermography Through Grayscale Morphologic Reconstruction: A Case Study—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