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 3701–3725 of 4925 papers

TitleStatusHype
Hybrid and Non-Uniform quantization methods using retro synthesis data for efficient inference—0
Direct Quantization for Training Highly Accurate Low Bit-width Deep Neural Networks—0
EQ-Net: A Unified Deep Learning Framework for Log-Likelihood Ratio Estimation and Quantization—0
Energy Efficient Federated Learning over Heterogeneous Mobile Devices via Joint Design of Weight Quantization and Wireless Transmission—0
Study of Energy-Efficient Distributed RLS-based Learning with Coarsely Quantized Signals—0
One-Bit Target Detection in Collocated MIMO Radar and Performance Degradation Analysis—0
Resource-efficient DNNs for Keyword Spotting using Neural Architecture Search and QuantizationCode0
SpAtten: Efficient Sparse Attention Architecture with Cascade Token and Head Pruning—0
FantastIC4: A Hardware-Software Co-Design Approach for Efficiently Running 4bit-Compact Multilayer Perceptrons—0
Efficient CNN-LSTM based Image Captioning using Neural Network CompressionCode0
CosSGD: Communication-Efficient Federated Learning with a Simple Cosine-Based Quantization—0
Exploring Neural Networks Quantization via Layer-Wise Quantization Analysis—0
Scalable Verification of Quantized Neural Networks (Technical Report)Code0
Quantizing data for distributed learning—0
Robust Downlink Transmit Optimization under Quantized Channel Feedback via the Strong Duality for QCQP—0
Predicting Generalization in Deep Learning via Local Measures of Distortion—0
Parallelized Rate-Distortion Optimized Quantization Using Deep Learning—0
Recurrence of Optimum for Training Weight and Activation Quantized Networks—0
Mix and Match: A Novel FPGA-Centric Deep Neural Network Quantization Framework—0
Poisoning Semi-supervised Federated Learning via Unlabeled Data: Attacks and Defenses—0
Design and Analysis of Uplink and Downlink Communications for Federated Learning—0
Reconfigurable Intelligent Surface Aided Constant-Envelope Wireless Power Transfer—0
Parallel Blockwise Knowledge Distillation for Deep Neural Network CompressionCode0
Boosting CNN-based primary quantization matrix estimation of double JPEG images via a classification-like architectureCode0
HM-ANN: Efficient Billion-Point Nearest Neighbor Search on Heterogeneous Memory—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