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 3901–3925 of 4925 papers

TitleStatusHype
Lite Transformer with Long-Short Range AttentionCode1
QUANOS- Adversarial Noise Sensitivity Driven Hybrid Quantization of Neural Networks—0
Up or Down? Adaptive Rounding for Post-Training Quantization—0
A Data and Compute Efficient Design for Limited-Resources Deep Learning—0
Integer Quantization for Deep Learning Inference: Principles and Empirical EvaluationCode0
LSQ+: Improving low-bit quantization through learnable offsets and better initializationCode1
HCM: Hardware-Aware Complexity Metric for Neural Network Architectures—0
Quantization Guided JPEG Artifact CorrectionCode0
Single upper limb pose estimation method based on improved stacked hourglass network—0
Deep Neural Network for Respiratory Sound Classification in Wearable Devices Enabled by Patient Specific Model TuningCode0
Q-CapsNets: A Specialized Framework for Quantizing Capsule Networks—0
Breaking the waves: asymmetric random periodic features for low-bitrate kernel machines—0
Technical Report: NEMO DNN Quantization for Deployment ModelCode1
Minimizing FLOPs to Learn Efficient Sparse RepresentationsCode1
Depthwise Discrete Representation LearningCode0
Exposing Hardware Building Blocks to Machine Learning Frameworks—0
Dithered backprop: A sparse and quantized backpropagation algorithm for more efficient deep neural network training—0
Deep Attentive Generative Adversarial Network for Photo-Realistic Image De-Quantization—0
Unsupervised Person Re-identification via Softened Similarity LearningCode0
CNN2Gate: Toward Designing a General Framework for Implementation of Convolutional Neural Networks on FPGA—0
LogicNets: Co-Designed Neural Networks and Circuits for Extreme-Throughput ApplicationsCode1
Attentive One-Dimensional Heatmap Regression for Facial Landmark Detection and Tracking—0
Feature Quantization Improves GAN TrainingCode1
Distributed Inference with Sparse and Quantized Communication—0
Single-Image HDR Reconstruction by Learning to Reverse the Camera PipelineCode1
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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