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 1801–1825 of 4925 papers

TitleStatusHype
Federated Learning: Strategies for Improving Communication Efficiency—0
CompMarkGS: Robust Watermarking for Compressed 3D Gaussian Splatting—0
A Diffusion Model Based Quality Enhancement Method for HEVC Compressed Video—0
Finetuning and Quantization of EEG-Based Foundational BioSignal Models on ECG and PPG Data for Blood Pressure Estimation—0
Federated Learning in Adversarial Settings—0
FinGPT-HPC: Efficient Pretraining and Finetuning Large Language Models for Financial Applications with High-Performance Computing—0
Completion Time Minimization of Fog-RAN-Assisted Federated Learning With Rate-Splitting Transmission—0
RATQ: A Universal Fixed-Length Quantizer for Stochastic Optimization—0
Federated Aggregation of Mallows Rankings: A Comparative Analysis of Borda and Lehmer Coding—0
Compensate Quantization Errors+: Quantized Models Are Inquisitive Learners—0
A Review of Recent Advances of Binary Neural Networks for Edge Computing—0
FedDQ: Communication-Efficient Federated Learning with Descending Quantization—0
FedDM: Enhancing Communication Efficiency and Handling Data Heterogeneity in Federated Diffusion Models—0
FIXAR: A Fixed-Point Deep Reinforcement Learning Platform with Quantization-Aware Training and Adaptive Parallelism—0
FedDiSC: A Computation-efficient Federated Learning Framework for Power Systems Disturbance and Cyber Attack Discrimination—0
Fixed-point optimization of deep neural networks with adaptive step size retraining—0
Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes—0
Fixed-point quantization aware training for on-device keyword-spotting—0
Compensate Quantization Errors: Make Weights Hierarchical to Compensate Each Other—0
Fixed Point Quantization of Deep Convolutional Networks—0
Fixflow: A Framework to Evaluate Fixed-point Arithmetic in Light-Weight CNN Inference—0
FLARE: FP-Less PTQ and Low-ENOB ADC Based AMS-PiM for Error-Resilient, Fast, and Efficient Transformer Acceleration—0
A review of learning vector quantization classifiers—0
FlashAttention on a Napkin: A Diagrammatic Approach to Deep Learning IO-Awareness—0
A Different View of Sigma-Delta Modulators Under the Lens of Pulse Frequency Modulation—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