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 1026–1050 of 4925 papers

TitleStatusHype
TriplePlay: Enhancing Federated Learning with CLIP for Non-IID Data and Resource Efficiency—0
BBS: Bi-directional Bit-level Sparsity for Deep Learning AccelerationCode1
Blind-Adaptive Quantizers—0
OPAL: Outlier-Preserved Microscaling Quantization Accelerator for Generative Large Language Models—0
Recursive Quantization for L_2 Stabilization of a Finite Capacity Stochastic Control Loop with Intermittent State Observations—0
WaterMAS: Sharpness-Aware Maximization for Neural Network Watermarking—0
Investigating Privacy Bias in Training Data of Language Models—0
LAST: Language Model Aware Speech Tokenization—0
Sorbet: A Neuromorphic Hardware-Compatible Transformer-Based Spiking Language Model—0
Learning Task-Based Trainable Neuromorphic ADCs via Power-Aware Distillation—0
Gaussian Rate-Distortion-Perception Coding and Entropy-Constrained Scalar Quantization—0
Task-Oriented Communication for Graph Data: A Graph Information Bottleneck Approach—0
CoAst: Validation-Free Contribution Assessment for Federated Learning based on Cross-Round Valuation—0
Optimization and Deployment of Deep Neural Networks for PPG-based Blood Pressure Estimation Targeting Low-power Wearables—0
Designing Large Foundation Models for Efficient Training and Inference: A SurveyCode1
Foundations of Large Language Model Compression -- Part 1: Weight QuantizationCode0
Robust Clustering on High-Dimensional Data with Stochastic QuantizationCode0
Compressing VAE-Based Out-of-Distribution Detectors for Embedded Deployment—0
VQ-Flow: Taming Normalizing Flows for Multi-Class Anomaly Detection via Hierarchical Vector QuantizationCode1
One-Index Vector Quantization Based Adversarial Attack on Image Classification—0
Edge AI: Evaluation of Model Compression Techniques for Convolutional Neural Networks—0
Enhancing Multi-Stream Beamforming Through CQIs For 5G NR FDD Massive MIMO Communications: A Tuning-Free Scheme—0
TinyAgent: Function Calling at the EdgeCode3
Federated Aggregation of Mallows Rankings: A Comparative Analysis of Borda and Lehmer Coding—0
Hyper-Compression: Model Compression via HyperfunctionCode1
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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