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 4376–4400 of 4925 papers

TitleStatusHype
Hybrid coarse-fine classification for head pose estimationCode0
Foothill: A Quasiconvex Regularization for Edge Computing of Deep Neural Networks—0
Activation Functions for Generalized Learning Vector Quantization - A Performance Comparison—0
Mean Estimation from One-Bit Measurements—0
Quantized Epoch-SGD for Communication-Efficient Distributed Learning—0
GIF2Video: Color Dequantization and Temporal Interpolation of GIF images—0
DSConv: Efficient Convolution OperatorCode0
Dataflow-based Joint Quantization of Weights and Activations for Deep Neural Networks—0
Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-AirCode0
Vector and Line Quantization for Billion-scale Similarity Search on GPUsCode0
ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using Alternating Direction Method of MultipliersCode1
Per-Tensor Fixed-Point Quantization of the Back-Propagation Algorithm—0
Interest Point Detection based on Adaptive Ternary Coding—0
Quantized Guided Pruning for Efficient Hardware Implementations of Convolutional Neural Networks—0
End-to-End Latent Fingerprint Search—0
Precision Highway for Ultra Low-Precision Quantization—0
Artificial neural networks condensation: A strategy to facilitate adaption of machine learning in medical settings by reducing computational burden—0
Quicker ADC : Unlocking the hidden potential of Product Quantization with SIMDCode0
SQuantizer: Simultaneous Learning for Both Sparse and Low-precision Neural Networks—0
Fast Adjustable Threshold For Uniform Neural Network Quantization (Winning solution of LPIRC-II)Code0
Efficient Super Resolution Using Binarized Neural Network—0
Auto-tuning Neural Network Quantization Framework for Collaborative Inference Between the Cloud and Edge—0
Deep neural networks algorithms for stochastic control problems on finite horizon: numerical applications—0
Exploring Embedding Methods in Binary Hyperdimensional Computing: A Case Study for Motor-Imagery based Brain-Computer InterfacesCode0
E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs—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