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 3951–3975 of 4925 papers

TitleStatusHype
What if Adversarial Samples were Digital ImagesCode0
GOBO: Quantizing Attention-Based NLP Models for Low Latency and Energy Efficient Inference—0
Visualisation and knowledge discovery from interpretable models—0
SmartExchange: Trading Higher-cost Memory Storage/Access for Lower-cost Computation—0
WSMN: An optimized multipurpose blind watermarking in Shearlet domain using MLP and NSGA-II—0
An Experimental Study of Reduced-Voltage Operation in Modern FPGAs for Neural Network Acceleration—0
The Bussgang Decomposition of Non-Linear Systems: Basic Theory and MIMO Extensions—0
CDC: Classification Driven Compression for Bandwidth Efficient Edge-Cloud Collaborative Deep Learning—0
Deep Neural Network-Based Quantized Signal Reconstruction for DOA Estimation—0
Linear Symmetric Quantization of Neural Networks for Low-precision Integer Hardware—0
Quantized Adam with Error Feedback—0
General Purpose Text Embeddings from Pre-trained Language Models for Scalable Inference—0
Dynamic Quantized Consensus of General Linear Multi-agent Systems under Denial-of-Service Attacks—0
Streamlining Tensor and Network Pruning in PyTorch—0
Quantization of Deep Neural Networks for Accumulator-constrained Processors—0
Automatic low-bit hybrid quantization of neural networks through meta learning—0
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
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
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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