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 2026–2050 of 4925 papers

TitleStatusHype
FastICARL: Fast Incremental Classifier and Representation Learning with Efficient Budget Allocation in Audio Sensing Applications—0
Communication-efficient k-Means for Edge-based Machine Learning—0
Arbitrary Bit-width Network: A Joint Layer-Wise Quantization and Adaptive Inference Approach—0
Guaranteed Quantization Error Computation for Neural Network Model Compression—0
Faster Neural Net Inference via Forests of Sparse Oblique Decision Trees—0
Faster Inference of Integer SWIN Transformer by Removing the GELU Activation—0
Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks—0
Gull: A Generative Multifunctional Audio Codec—0
GWQ: Gradient-Aware Weight Quantization for Large Language Models—0
Haar Wavelet Feature Compression for Quantized Graph Convolutional Networks—0
Fastening the Initial Access in 5G NR Sidelink for 6G V2X Networks—0
HACK: Homomorphic Acceleration via Compression of the Key-Value Cache for Disaggregated LLM Inference—0
Arabic Compact Language Modelling for Resource Limited Devices—0
Hadamard Domain Training with Integers for Class Incremental Quantized Learning—0
HadaNets: Flexible Quantization Strategies for Neural Networks—0
HadaNorm: Diffusion Transformer Quantization through Mean-Centered Transformations—0
HALL-E: Hierarchical Neural Codec Language Model for Minute-Long Zero-Shot Text-to-Speech Synthesis—0
Additive Quantization for Extreme Vector Compression—0
Acceleration for Compressed Gradient Descent in Distributed Optimization—0
FAST: DNN Training Under Variable Precision Block Floating Point with Stochastic Rounding—0
LANA: Latency Aware Network Acceleration—0
Fast DistilBERT on CPUs—0
Communication-Efficient Federated Learning over Capacity-Limited Wireless Networks—0
Communication-Efficient Federated Learning via Quantized Compressed Sensing—0
AQUILA: Communication Efficient Federated Learning with Adaptive Quantization in Device Selection Strategy—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