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 3901–3950 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
A Modular Neural Network Based Deep Learning Approach for MIMO Signal Detection—0
Binary Neural Networks: A SurveyCode2
Improved Gradient based Adversarial Attacks for Quantized NetworksCode0
Deep Learning for Radio Resource Allocation with Diverse Quality-of-Service Requirements in 5G—0
Event-Triggered Quantized Average Consensus via Mass Summation—0
A Short Note on Analyzing Sequence Complexity in Trajectory Prediction Benchmarks—0
Acceleration of Convolutional Neural Network Using FFT-Based Split Convolutions—0
Multi-Feature Discrete Collaborative Filtering for Fast Cold-start Recommendation—0
A Survey of Methods for Low-Power Deep Learning and Computer Vision—0
Tree Index: A New Cluster Evaluation Technique—0
Multi-target regression via output space quantization—0
DP-Net: Dynamic Programming Guided Deep Neural Network Compression—0
FTT-NAS: Discovering Fault-Tolerant Convolutional Neural ArchitectureCode0
LANCE: Efficient Low-Precision Quantized Winograd Convolution for Neural Networks Based on Graphics Processing Units—0
Learning to Structure an Image with Few ColorsCode1
Efficient Bitwidth Search for Practical Mixed Precision Neural Network—0
RCNet: Incorporating Structural Information into Deep RNN for MIMO-OFDM Symbol Detection with Limited Training—0
DNN+NeuroSim V2.0: An End-to-End Benchmarking Framework for Compute-in-Memory Accelerators for On-chip TrainingCode1
LCP: A Low-Communication Parallelization Method for Fast Neural Network Inference in Image Recognition—0
Fast Distance-based Anomaly Detection in Images Using an Inception-like AutoencoderCode1
Kernel Quantization for Efficient Network Compression—0
Communication-efficient Variance-reduced Stochastic Gradient Descent—0
Distributed Deep Convolutional Compression for Massive MIMO CSI Feedback—0
Generative Low-bitwidth Data Free QuantizationCode1
Ternary Compression for Communication-Efficient Federated LearningCode1
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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