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 2151–2175 of 4925 papers

TitleStatusHype
Dirichlet Mixture Model based VQ Performance Prediction for Line Spectral Frequency—0
DI-Retinex: Digital-Imaging Retinex Theory for Low-Light Image Enhancement—0
Analysis of Null Related Beampattern Measures and Signal Quantization Effects for Linear Differential Microphone Arrays—0
Deep Learning on Mobile Devices - A Review—0
ML Codebook Design for Initial Access and CSI Type-II Feedback in Sub-6GHz 5G NR—0
Instance-Aware Group Quantization for Vision Transformers—0
Direct Quantization for Training Highly Accurate Low Bit-width Deep Neural Networks—0
DipSVD: Dual-importance Protected SVD for Efficient LLM Compression—0
Dimension-Free Bounds for Low-Precision Training—0
Dimensionality-Dependent Generalization Bounds for k-Dimensional Coding Schemes—0
Analysis and Optimization for RIS-Aided Multi-Pair Communications Relying on Statistical CSI—0
Action-Quantized Offline Reinforcement Learning for Robotic Skill Learning—0
DILEMMA: Joint LLM Quantization and Distributed LLM Inference Over Edge Computing Systems—0
DilateQuant: Accurate and Efficient Diffusion Quantization via Weight Dilation—0
Beyond Neighbourhood-Preserving Transformations for Quantization-Based Unsupervised Hashing—0
Diffusion Product Quantization—0
Analog Spiking Neuron in CMOS 28 nm Towards Large-Scale Neuromorphic Processors—0
CDFGNN: a Systematic Design of Cache-based Distributed Full-Batch Graph Neural Network Training with Communication Reduction—0
Inference of Quantized Neural Networks on Heterogeneous All-Programmable Devices—0
Beyond Discreteness: Finite-Sample Analysis of Straight-Through Estimator for Quantization—0
Better Schedules for Low Precision Training of Deep Neural Networks—0
Diffusion-based Perceptual Neural Video Compression with Temporal Diffusion Information Reuse—0
Differential Privacy with Random Projections and Sign Random Projections—0
ACT360: An Efficient 360-Degree Action Detection and Summarization Framework for Mission-Critical Training and Debriefing—0
Differential Modulation in Massive MIMO With Low-Resolution ADCs—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